[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-minimal-numpy-rnn-for-char-level-text-gen-summary":3,"summaries-facets-categories":207,"summary-related-minimal-numpy-rnn-for-char-level-text-gen-summary":7135},{"id":4,"title":5,"ai":6,"body":13,"categories":184,"created_at":186,"date_modified":186,"description":178,"extension":187,"faq":186,"featured":188,"kicker_label":186,"meta":189,"navigation":190,"path":191,"published_at":192,"question":186,"scraped_at":186,"seo":193,"sitemap":194,"source_id":195,"source_name":196,"source_type":197,"source_url":198,"stem":199,"tags":200,"thumbnail_url":186,"tldr":204,"tweet":186,"unknown_tags":205,"__hash__":206},"summaries\u002Fsummaries\u002Fminimal-numpy-rnn-for-char-level-text-gen-summary.md","Minimal NumPy RNN for Char-Level Text Gen",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",10743,1482,11844,0.0024192,{"type":14,"value":15,"toc":177},"minimark",[16,21,38,61,81,85,104,128,135,139,149,164,174],[17,18,20],"h2",{"id":19},"rnn-architecture-and-one-hot-encoding","RNN Architecture and One-Hot Encoding",[22,23,24,25,29,30,33,34,37],"p",{},"Load text from 'input.txt' into ",[26,27,28],"code",{},"data",", extract unique ",[26,31,32],{},"chars"," for vocabulary (vocab_size = len(chars)). Map chars to indices with ",[26,35,36],{},"char_to_ix"," and reverse. Use one-hot encoding: inputs are lists of indices turned into (vocab_size, 1) vectors with 1 at input index.",[22,39,40,41,44,45,48,49,52,53,56,57,60],{},"Hidden layer size fixed at 100 neurons (",[26,42,43],{},"hidden_size=100","), sequence length 25 (",[26,46,47],{},"seq_length=25","), learning rate 0.1. Weights initialized small: ",[26,50,51],{},"Wxh = np.random.randn(100, vocab_size)*0.01"," (input-to-hidden), ",[26,54,55],{},"Whh"," (hidden-to-hidden, 100x100), ",[26,58,59],{},"Why"," (hidden-to-output, vocab_size x 100). Biases zero-initialized. Scaling by 0.01 keeps initial activations small for tanh stability and breaks symmetry so hidden units learn distinct features.",[22,62,63,64,67,68,71,72,75,76,80],{},"Forward step per timestep t: ",[26,65,66],{},"hs[t] = tanh(Wxh @ xs[t] + Whh @ hs[t-1] + bh)",", then ",[26,69,70],{},"ys[t] = Why @ hs[t] + by",", softmax ",[26,73,74],{},"ps[t] = exp(ys[t])\u002Fsum(exp(ys[t]))"," for next-char probs. Loss is negative log-likelihood: sum -log(ps[t]",[77,78,79],"span",{},"target",").",[17,82,84],{"id":83},"backpropagation-through-time-and-gradients","Backpropagation Through Time and Gradients",[22,86,87,88,91,92,95,96,99,100,103],{},"In ",[26,89,90],{},"lossFun(inputs, targets, hprev)",": forward pass stores xs, hs, ys, ps for all timesteps. Backward pass starts from output: ",[26,93,94],{},"dy = ps[t].copy(); dy[target] -= 1"," (softmax + cross-entropy gradient simplifies to this). Accumulate ",[26,97,98],{},"dWhy += dy @ hs[t].T",", ",[26,101,102],{},"dby += dy",".",[22,105,106,107,110,111,114,115,99,118,99,121,99,124,127],{},"Propagate to hidden: ",[26,108,109],{},"dh = Why.T @ dy + dhnext"," (dhnext from future timestep), ",[26,112,113],{},"dhraw = (1 - hs[t]^2) * dh"," (tanh derivative), then ",[26,116,117],{},"dbh += dhraw",[26,119,120],{},"dWxh += dhraw @ xs[t].T",[26,122,123],{},"dWhh += dhraw @ hs[t-1].T",[26,125,126],{},"dhnext = Whh.T @ dhraw"," for prior timestep.",[22,129,130,131,134],{},"Clip all gradients to ",[77,132,133],{},"-5, 5"," to prevent exploding gradients. Returns total loss, all dparams, final h for next sequence.",[17,136,138],{"id":137},"adagrad-training-and-text-sampling","Adagrad Training and Text Sampling",[22,140,141,142,145,146,103],{},"Infinite loop sweeps data left-to-right in seq_length=25 chunks: reset hprev=zeros every epoch (when p >= len(data)). Compute inputs\u002Ftargets as char indices for data",[77,143,144],{},"p:p+25"," and shifted ",[77,147,148],{},"p+1:p+26",[22,150,151,152,155,156,159,160,163],{},"Every 100 iterations: sample 200 chars from model starting with inputs",[77,153,154],{},"0"," seed: forward like training but pick ",[26,157,158],{},"ix = np.random.choice(vocab_size, p=ps.ravel())",", decode to text, print. Smooth loss: ",[26,161,162],{},"smooth_loss *= 0.999 + loss * 0.001",", print every 100 iters.",[22,165,166,167,99,170,173],{},"Update with Adagrad: mem vars track ",[26,168,169],{},"mem += dparam**2",[26,171,172],{},"param -= lr * dparam \u002F sqrt(mem + 1e-8)",". Advance p by 25, n +=1. Initial smooth_loss = -log(1\u002Fvocab_size)*25.",[22,175,176],{},"Common issues: input.txt must exceed seq_length+1 chars (else IndexError in loss); large datasets like Shakespeare need 100k+ iters for loss ~3.0 and coherent text.",{"title":178,"searchDepth":179,"depth":179,"links":180},"",2,[181,182,183],{"id":19,"depth":179,"text":20},{"id":83,"depth":179,"text":84},{"id":137,"depth":179,"text":138},[185],"Data Science & Visualization",null,"md",false,{},true,"\u002Fsummaries\u002Fminimal-numpy-rnn-for-char-level-text-gen-summary","2026-04-08 21:21:20",{"title":5,"description":178},{"loc":191},"7fdb0ca0899660d5","Andrej Karpathy Gists","article","https:\u002F\u002Funknown","summaries\u002Fminimal-numpy-rnn-for-char-level-text-gen-summary",[201,202,203],"python","machine-learning","deep-learning","Build a vanilla RNN language model from scratch in ~170 lines of NumPy: processes text chunks of 25 chars, trains with BPTT and Adagrad, generates samples after 100 iterations.",[],"ytSsn8v5OXyfyPKcCX7WUMYHNuzZlEdeMutJWRk1eM0",[208,211,214,216,219,221,224,227,229,231,233,235,238,240,242,244,246,249,251,253,255,257,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,311,313,315,317,319,321,323,325,327,329,331,333,335,337,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,381,383,385,387,389,391,393,395,397,399,401,403,405,407,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,475,477,479,481,483,485,487,489,491,493,495,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,531,533,535,537,539,541,543,545,547,549,551,554,556,558,560,562,564,566,568,570,572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,612,615,617,619,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,661,663,665,667,669,671,673,675,677,679,681,683,685,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932,934,936,938,940,942,944,946,948,950,952,954,956,958,960,962,964,966,968,970,972,975,977,979,981,983,986,988,990,992,994,996,998,1000,1002,1004,1006,1008,1010,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1492,1494,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1978,1980,1982,1984,1986,1988,1990,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2124,2126,2128,2130,2132,2134,2136,2138,2140,2142,2144,2146,2148,2150,2152,2154,2156,2158,2160,2162,2164,2166,2168,2170,2172,2174,2176,2178,2180,2182,2184,2186,2188,2190,2192,2194,2196,2198,2200,2202,2204,2206,2208,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2286,2288,2290,2292,2294,2296,2298,2300,2302,2304,2306,2308,2310,2312,2314,2316,2318,2320,2322,2324,2326,2328,2330,2332,2334,2336,2338,2340,2342,2344,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2460,2462,2464,2466,2468,2470,2472,2474,2476,2478,2480,2482,2484,2486,2488,2490,2492,2494,2496,2498,2500,2502,2504,2506,2508,2510,2512,2514,2516,2518,2520,2522,2524,2526,2528,2530,2532,2534,2536,2538,2540,2542,2544,2546,2548,2550,2552,2554,2556,2558,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2604,2606,2608,2610,2612,2614,2616,2618,2620,2622,2624,2626,2628,2630,2632,2634,2636,2638,2640,2642,2644,2646,2648,2650,2652,2654,2656,2658,2660,2662,2664,2666,2668,2670,2672,2674,2676,2678,2680,2682,2684,2686,2688,2690,2692,2694,2696,2698,2700,2702,2704,2706,2708,2710,2712,2714,2716,2718,2720,2722,2724,2726,2728,2730,2732,2734,2736,2738,2740,2742,2744,2746,2748,2750,2752,2754,2756,2758,2760,2762,2764,2766,2768,2770,2772,2774,2776,2778,2780,2782,2784,2786,2788,2790,2792,2794,2796,2798,2800,2802,2804,2806,2808,2810,2812,2814,2816,2818,2820,2822,2824,2826,2828,2830,2832,2834,2836,2838,2840,2842,2844,2846,2848,2850,2852,2854,2856,2858,2860,2862,2864,2866,2868,2870,2872,2874,2876,2878,2880,2882,2884,2886,2888,2890,2892,2894,2896,2898,2900,2902,2904,2906,2908,2910,2912,2914,2916,2918,2920,2922,2924,2926,2928,2930,2932,2934,2936,2938,2940,2942,2944,2946,2948,2950,2952,2954,2956,2958,2960,2962,2964,2966,2968,2970,2972,2974,2976,2978,2980,2982,2984,2986,2988,2990,2992,2994,2996,2998,3000,3002,3004,3006,3008,3010,3012,3014,3016,3018,3020,3022,3024,3026,3028,3030,3032,3034,3036,3038,3040,3042,3044,3046,3048,3050,3052,3054,3056,3058,3060,3062,3064,3066,3068,3070,3072,3074,3076,3078,3080,3082,3084,3086,3088,3090,3092,3094,3096,3098,3100,3102,3104,3106,3108,3110,3112,3114,3116,3118,3120,3122,3124,3126,3128,3130,3132,3134,3136,3138,3140,3142,3144,3146,3148,3150,3152,3154,3156,3158,3160,3162,3164,3166,3168,3170,3172,3174,3176,3178,3180,3182,3184,3186,3188,3190,3192,3194,3196,3198,3200,3202,3204,3206,3208,3210,3212,3214,3216,3218,3220,3222,3224,3226,3228,3230,3232,3234,3236,3238,3240,3242,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3400,3402,3404,3406,3408,3410,3412,3414,3416,3418,3420,3422,3424,3426,3428,3430,3432,3434,3436,3438,3440,3442,3444,3446,3448,3450,3452,3454,3456,3458,3460,3462,3464,3466,3468,3470,3472,3474,3476,3478,3480,3482,3484,3486,3488,3490,3492,3494,3496,3498,3500,3502,3504,3506,3508,3510,3512,3514,3516,3518,3520,3522,3524,3526,3528,3530,3532,3534,3536,3538,3540,3542,3544,3546,3548,3550,3552,3554,3556,3558,3560,3562,3564,3566,3568,3570,3572,3574,3576,3578,3580,3582,3584,3586,3588,3590,3592,3594,3596,3598,3600,3602,3604,3606,3608,3610,3612,3614,3616,3618,3620,3622,3624,3626,3628,3630,3632,3634,3636,3638,3640,3642,3644,3646,3648,3650,3652,3654,3656,3658,3660,3662,3664,3666,3668,3670,3672,3674,3676,3678,3680,3682,3684,3686,3688,3690,3692,3694,3696,3698,3700,3702,3704,3706,3708,3710,3712,3714,3716,3718,3720,3722,3724,3726,3728,3730,3732,3734,3736,3738,3740,3742,3744,3746,3748,3750,3752,3754,3756,3758,3760,3762,3764,3766,3768,3770,3772,3774,3776,3778,3780,3782,3784,3786,3788,3790,3792,3794,3796,3798,3800,3802,3804,3806,3808,3810,3812,3814,3816,3818,3820,3822,3824,3826,3828,3830,3832,3834,3836,3838,3840,3842,3844,3846,3848,3850,3852,3854,3856,3858,3860,3862,3864,3866,3868,3870,3872,3874,3876,3878,3880,3882,3884,3886,3888,3890,3892,3894,3896,3898,3900,3902,3904,3906,3908,3910,3912,3914,3916,3918,3920,3922,3924,3926,3928,3930,3932,3934,3936,3938,3940,3942,3944,3946,3948,3950,3952,3954,3956,3958,3960,3962,3964,3966,3968,3970,3972,3974,3976,3978,3980,3982,3984,3986,3988,3990,3992,3994,3996,3998,4000,4002,4004,4006,4008,4010,4012,4014,4016,4018,4020,4022,4024,4026,4028,4030,4032,4034,4036,4038,4040,4042,4044,4046,4048,4050,4052,4054,4056,4058,4060,4062,4064,4066,4068,4070,4072,4074,4076,4078,4080,4082,4084,4086,4088,4090,4092,4094,4096,4098,4100,4102,4104,4106,4108,4110,4112,4114,4116,4118,4120,4122,4124,4126,4128,4130,4132,4134,4136,4138,4140,4142,4144,4146,4148,4150,4152,4154,4156,4158,4160,4162,4164,4166,4168,4170,4172,4174,4176,4178,4180,4182,4184,4186,4188,4190,4192,4194,4196,4198,4200,4202,4204,4206,4208,4210,4212,4214,4216,4218,4220,4222,4224,4226,4228,4230,4232,4234,4236,4238,4240,4242,4244,4246,4248,4250,4252,4254,4256,4258,4260,4262,4264,4266,4268,4270,4272,4274,4276,4278,4280,4282,4284,4286,4288,4290,4292,4294,4296,4298,4300,4302,4304,4306,4308,4310,4312,4314,4316,4318,4320,4322,4324,4326,4328,4330,4332,4334,4336,4338,4340,4342,4344,4346,4348,4350,4352,4354,4356,4358,4360,4362,4364,4366,4368,4370,4372,4374,4376,4378,4380,4382,4384,4386,4388,4390,4392,4394,4396,4398,4400,4402,4404,4406,4408,4410,4412,4414,4416,4418,4420,4422,4424,4426,4428,4430,4432,4434,4436,4438,4440,4442,4444,4446,4448,4450,4452,4454,4456,4458,4460,4462,4464,4466,4468,4470,4472,4474,4476,4478,4480,4482,4484,4486,4488,4490,4492,4494,4496,4498,4500,4502,4504,4506,4508,4510,4512,4514,4516,4518,4520,4522,4524,4526,4528,4530,4532,4534,4536,4538,4540,4542,4544,4546,4548,4550,4552,4554,4556,4558,4560,4562,4564,4566,4568,4570,4572,4574,4576,4578,4580,4582,4584,4586,4588,4590,4592,4594,4596,4598,4600,4602,4604,4606,4608,4610,4612,4614,4616,4618,4620,4622,4624,4626,4628,4630,4632,4634,4636,4638,4640,4642,4644,4646,4648,4650,4652,4654,4656,4658,4660,4662,4665,4667,4669,4671,4673,4675,4677,4679,4681,4683,4685,4687,4689,4691,4693,4695,4697,4699,4701,4703,4705,4707,4709,4711,4713,4715,4717,4719,4721,4723,4725,4727,4729,4731,4733,4735,4737,4739,4741,4743,4745,4747,4749,4751,4753,4755,4757,4759,4761,4763,4765,4767,4769,4771,4773,4775,4777,4779,4781,4783,4785,4787,4789,4791,4793,4795,4797,4799,4801,4803,4805,4807,4809,4811,4813,4815,4817,4819,4821,4823,4825,4827,4829,4831,4833,4835,4837,4839,4841,4843,4845,4847,4849,4851,4853,4855,4857,4859,4861,4863,4865,4867,4869,4871,4873,4875,4877,4879,4881,4883,4885,4887,4889,4891,4893,4895,4897,4899,4901,4903,4905,4907,4909,4911,4913,4915,4917,4919,4921,4923,4925,4927,4929,4931,4933,4935,4937,4939,4941,4943,4945,4947,4949,4951,4953,4955,4957,4959,4961,4963,4965,4967,4969,4971,4973,4975,4977,4979,4981,4983,4985,4987,4989,4991,4993,4995,4997,4999,5001,5003,5005,5007,5009,5011,5013,5015,5017,5019,5021,5023,5025,5027,5029,5031,5033,5035,5037,5039,5041,5043,5045,5047,5049,5051,5053,5055,5057,5059,5061,5063,5065,5067,5069,5071,5073,5075,5077,5079,5081,5083,5085,5087,5089,5091,5093,5095,5097,5099,5101,5103,5105,5107,5109,5111,5113,5115,5117,5119,5121,5123,5125,5127,5129,5131,5133,5135,5137,5139,5141,5143,5145,5147,5149,5151,5153,5155,5157,5159,5161,5163,5165,5167,5169,5171,5173,5175,5177,5179,5181,5183,5185,5187,5189,5191,5193,5195,5197,5199,5201,5203,5205,5207,5209,5211,5213,5215,5217,5219,5221,5223,5225,5227,5229,5231,5233,5235,5237,5239,5241,5243,5245,5247,5249,5251,5253,5255,5257,5259,5261,5263,5265,5267,5269,5271,5273,5275,5277,5279,5281,5283,5285,5287,5289,5291,5293,5295,5297,5299,5301,5303,5305,5307,5309,5311,5313,5315,5317,5319,5321,5323,5325,5327,5329,5331,5333,5335,5337,5339,5341,5343,5345,5347,5349,5351,5353,5355,5357,5359,5361,5363,5365,5367,5369,5371,5373,5375,5377,5379,5381,5383,5385,5387,5389,5391,5393,5395,5397,5399,5401,5403,5405,5407,5409,5411,5413,5415,5417,5419,5421,5423,5425,5427,5429,5431,5433,5435,5437,5439,5441,5443,5445,5447,5449,5451,5453,5455,5457,5459,5461,5463,5465,5467,5469,5471,5473,5475,5477,5479,5481,5483,5485,5487,5489,5491,5493,5495,5497,5499,5501,5503,5505,5507,5509,5511,5513,5515,5517,5519,5522,5524,5526,5528,5530,5532,5534,5536,5538,5540,5542,5544,5546,5548,5550,5552,5554,5556,5558,5560,5562,5564,5566,5568,5570,5572,5574,5576,5578,5580,5582,5584,5586,5588,5590,5592,5594,5596,5598,5600,5602,5604,5606,5608,5610,5612,5614,5616,5618,5620,5622,5624,5626,5628,5630,5632,5634,5636,5638,5640,5642,5644,5646,5648,5650,5652,5654,5656,5658,5660,5662,5664,5666,5668,5670,5672,5674,5676,5678,5680,5682,5684,5686,5688,5690,5692,5694,5696,5698,5700,5702,5704,5706,5708,5710,5712,5714,5716,5718,5720,5722,5725,5727,5729,5731,5733,5735,5737,5739,5741,5743,5745,5747,5749,5751,5753,5755,5757,5759,5761,5763,5765,5767,5769,5771,5773,5775,5777,5779,5781,5783,5785,5787,5789,5791,5793,5795,5797,5799,5801,5803,5805,5807,5809,5811,5813,5815,5817,5819,5821,5823,5825,5827,5829,5831,5833,5835,5837,5839,5841,5843,5845,5847,5849,5851,5853,5855,5857,5859,5861,5863,5865,5867,5869,5871,5873,5875,5877,5879,5881,5883,5885,5887,5889,5891,5893,5895,5897,5899,5901,5903,5905,5907,5909,5911,5913,5915,5917,5919,5921,5923,5925,5927,5929,5931,5933,5935,5937,5939,5941,5943,5945,5947,5949,5951,5953,5955,5957,5959,5961,5963,5965,5967,5969,5971,5973,5975,5977,5979,5981,5983,5985,5987,5989,5991,5993,5995,5997,5999,6001,6003,6005,6007,6009,6011,6013,6015,6017,6019,6021,6023,6025,6027,6029,6031,6033,6035,6037,6039,6041,6043,6045,6047,6049,6051,6053,6055,6057,6059,6061,6063,6065,6067,6069,6071,6073,6075,6077,6079,6081,6083,6085,6087,6089,6091,6093,6095,6097,6099,6101,6103,6105,6107,6109,6111,6113,6115,6117,6119,6121,6123,6125,6127,6129,6131,6133,6135,6137,6139,6141,6143,6145,6147,6149,6151,6153,6155,6157,6159,6161,6163,6165,6167,6169,6171,6173,6175,6177,6179,6181,6183,6185,6187,6189,6191,6193,6195,6197,6199,6201,6203,6205,6207,6209,6211,6213,6215,6217,6219,6221,6223,6225,6227,6229,6231,6233,6235,6237,6239,6241,6243,6245,6247,6249,6251,6253,6255,6257,6259,6261,6263,6265,6267,6269,6271,6273,6275,6277,6279,6281,6283,6285,6287,6289,6291,6293,6295,6297,6299,6301,6303,6305,6307,6309,6311,6313,6315,6317,6319,6321,6323,6325,6327,6329,6331,6333,6335,6337,6339,6341,6343,6345,6347,6349,6351,6353,6355,6357,6359,6361,6363,6365,6367,6369,6371,6373,6375,6377,6379,6381,6383,6385,6387,6389,6391,6393,6395,6397,6399,6401,6403,6405,6407,6409,6411,6413,6415,6417,6419,6421,6423,6425,6427,6429,6431,6433,6435,6437,6439,6441,6443,6445,6447,6449,6451,6453,6455,6457,6459,6461,6463,6465,6467,6469,6471,6473,6475,6477,6479,6481,6483,6485,6487,6489,6491,6493,6495,6497,6499,6501,6503,6505,6507,6509,6511,6513,6515,6517,6519,6521,6523,6525,6527,6529,6531,6533,6535,6537,6539,6541,6543,6545,6547,6549,6551,6553,6555,6557,6559,6561,6563,6565,6567,6569,6571,6573,6575,6577,6579,6581,6583,6585,6587,6589,6591,6593,6595,6597,6599,6601,6603,6605,6607,6609,6611,6613,6615,6617,6619,6621,6623,6625,6627,6629,6631,6633,6635,6637,6639,6641,6643,6645,6647,6649,6651,6653,6655,6657,6659,6661,6663,6665,6667,6669,6671,6673,6675,6677,6679,6681,6683,6685,6687,6689,6691,6693,6695,6697,6699,6701,6703,6705,6707,6709,6711,6713,6715,6717,6719,6721,6723,6725,6727,6729,6731,6733,6735,6737,6739,6741,6743,6745,6747,6749,6751,6753,6755,6757,6759,6761,6763,6765,6767,6769,6771,6773,6775,6777,6779,6781,6783,6785,6787,6789,6791,6793,6795,6797,6799,6801,6803,6805,6807,6809,6811,6813,6815,6817,6819,6821,6823,6825,6827,6829,6831,6833,6835,6837,6839,6841,6843,6845,6847,6849,6851,6853,6855,6857,6859,6861,6863,6865,6867,6869,6871,6873,6875,6877,6879,6881,6883,6885,6887,6889,6891,6893,6895,6897,6899,6901,6903,6905,6907,6909,6911,6913,6915,6917,6919,6921,6923,6925,6927,6929,6931,6933,6935,6937,6939,6941,6943,6945,6947,6949,6951,6953,6955,6957,6959,6961,6963,6965,6967,6969,6971,6973,6975,6977,6979,6981,6983,6985,6987,6989,6991,6993,6995,6997,6999,7001,7003,7005,7007,7009,7011,7013,7015,7017,7019,7021,7023,7025,7027,7029,7031,7033,7035,7037,7039,7041,7043,7045,7047,7049,7051,7053,7055,7057,7059,7061,7063,7065,7067,7069,7071,7073,7075,7077,7079,7081,7083,7085,7087,7089,7091,7093,7095,7097,7099,7101,7103,7105,7107,7109,7111,7113,7115,7117,7119,7121,7123,7125,7127,7129,7131,7133],{"categories":209},[210],"AI & LLMs",{"categories":212},[213],"Developer Productivity",{"categories":215},[210],{"categories":217},[218],"Business & SaaS",{"categories":220},[210],{"categories":222},[223],"AI Automation",{"categories":225},[226],"Product Strategy",{"categories":228},[223],{"categories":230},[210],{"categories":232},[213],{"categories":234},[223],{"categories":236},[237],"Software Engineering",{"categories":239},[210],{"categories":241},[218],{"categories":243},[],{"categories":245},[210],{"categories":247},[248],"Inference & Serving",{"categories":250},[210],{"categories":252},[210],{"categories":254},[223],{"categories":256},[],{"categories":258},[259],"AI News & Trends",{"categories":261},[185],{"categories":263},[223],{"categories":265},[210],{"categories":267},[210],{"categories":269},[218],{"categories":271},[213],{"categories":273},[210],{"categories":275},[223],{"categories":277},[259],{"categories":279},[210],{"categories":281},[223],{"categories":283},[223],{"categories":285},[210],{"categories":287},[210],{"categories":289},[223],{"categories":291},[210],{"categories":293},[210],{"categories":295},[210],{"categories":297},[223],{"categories":299},[259],{"categories":301},[210],{"categories":303},[210],{"categories":305},[210],{"categories":307},[],{"categories":309},[310],"Design & Frontend",{"categories":312},[185],{"categories":314},[259],{"categories":316},[210],{"categories":318},[210],{"categories":320},[210],{"categories":322},[],{"categories":324},[210],{"categories":326},[210],{"categories":328},[223],{"categories":330},[237],{"categories":332},[210],{"categories":334},[223],{"categories":336},[210],{"categories":338},[339],"Marketing & Growth",{"categories":341},[310],{"categories":343},[210],{"categories":345},[223],{"categories":347},[210],{"categories":349},[210],{"categories":351},[237],{"categories":353},[210],{"categories":355},[],{"categories":357},[],{"categories":359},[310],{"categories":361},[210],{"categories":363},[223],{"categories":365},[213],{"categories":367},[237],{"categories":369},[223],{"categories":371},[310],{"categories":373},[226],{"categories":375},[210],{"categories":377},[237],{"categories":379},[380],"DevOps & Cloud",{"categories":382},[223],{"categories":384},[226],{"categories":386},[259],{"categories":388},[210],{"categories":390},[],{"categories":392},[210],{"categories":394},[210],{"categories":396},[],{"categories":398},[223],{"categories":400},[237],{"categories":402},[],{"categories":404},[237],{"categories":406},[210],{"categories":408},[409],"Governance & Standards",{"categories":411},[218],{"categories":413},[],{"categories":415},[],{"categories":417},[210],{"categories":419},[210],{"categories":421},[223],{"categories":423},[210],{"categories":425},[210],{"categories":427},[223],{"categories":429},[210],{"categories":431},[210],{"categories":433},[210],{"categories":435},[],{"categories":437},[237],{"categories":439},[],{"categories":441},[],{"categories":443},[210],{"categories":445},[237],{"categories":447},[],{"categories":449},[237],{"categories":451},[210],{"categories":453},[210],{"categories":455},[339],{"categories":457},[210],{"categories":459},[210],{"categories":461},[210],{"categories":463},[310],{"categories":465},[310],{"categories":467},[210],{"categories":469},[237],{"categories":471},[223],{"categories":473},[474],"GovTech & Public-Sector Adoption",{"categories":476},[237],{"categories":478},[210],{"categories":480},[210],{"categories":482},[210],{"categories":484},[223],{"categories":486},[223],{"categories":488},[185],{"categories":490},[210],{"categories":492},[259],{"categories":494},[223],{"categories":496},[497],"Legal AI Tools",{"categories":499},[210],{"categories":501},[223],{"categories":503},[210],{"categories":505},[339],{"categories":507},[223],{"categories":509},[226],{"categories":511},[210],{"categories":513},[237],{"categories":515},[474],{"categories":517},[],{"categories":519},[223],{"categories":521},[],{"categories":523},[218],{"categories":525},[223],{"categories":527},[223],{"categories":529},[530],"RAG & Retrieval",{"categories":532},[218],{"categories":534},[210],{"categories":536},[237],{"categories":538},[237],{"categories":540},[380],{"categories":542},[310],{"categories":544},[223],{"categories":546},[210],{"categories":548},[210],{"categories":550},[],{"categories":552},[553],"Agents & Orchestration",{"categories":555},[237],{"categories":557},[210],{"categories":559},[],{"categories":561},[223],{"categories":563},[218],{"categories":565},[],{"categories":567},[210],{"categories":569},[],{"categories":571},[210],{"categories":573},[213],{"categories":575},[237],{"categories":577},[218],{"categories":579},[210],{"categories":581},[223],{"categories":583},[210],{"categories":585},[210],{"categories":587},[259],{"categories":589},[210],{"categories":591},[],{"categories":593},[210],{"categories":595},[],{"categories":597},[210],{"categories":599},[237],{"categories":601},[210],{"categories":603},[223],{"categories":605},[185],{"categories":607},[],{"categories":609},[210],{"categories":611},[310],{"categories":613},[614],"Models & Frontier Labs",{"categories":616},[],{"categories":618},[310],{"categories":620},[621],"Regulation & Governance of AI",{"categories":623},[226],{"categories":625},[223],{"categories":627},[],{"categories":629},[210],{"categories":631},[210],{"categories":633},[223],{"categories":635},[223],{"categories":637},[259],{"categories":639},[210],{"categories":641},[218],{"categories":643},[210],{"categories":645},[223],{"categories":647},[],{"categories":649},[237],{"categories":651},[223],{"categories":653},[210],{"categories":655},[226],{"categories":657},[210],{"categories":659},[660],"AI Policy & Regulation",{"categories":662},[],{"categories":664},[210],{"categories":666},[223],{"categories":668},[223],{"categories":670},[226],{"categories":672},[223],{"categories":674},[210],{"categories":676},[210],{"categories":678},[210],{"categories":680},[223],{"categories":682},[],{"categories":684},[185],{"categories":686},[687],"Evals & Reliability",{"categories":689},[210],{"categories":691},[210],{"categories":693},[],{"categories":695},[213],{"categories":697},[474],{"categories":699},[660],{"categories":701},[210],{"categories":703},[218],{"categories":705},[210],{"categories":707},[223],{"categories":709},[210],{"categories":711},[223],{"categories":713},[553],{"categories":715},[210],{"categories":717},[237],{"categories":719},[210],{"categories":721},[],{"categories":723},[310],{"categories":725},[],{"categories":727},[210],{"categories":729},[474],{"categories":731},[210],{"categories":733},[210],{"categories":735},[210],{"categories":737},[],{"categories":739},[210],{"categories":741},[310],{"categories":743},[237],{"categories":745},[],{"categories":747},[210],{"categories":749},[],{"categories":751},[223],{"categories":753},[210],{"categories":755},[310],{"categories":757},[],{"categories":759},[210],{"categories":761},[210],{"categories":763},[185],{"categories":765},[223],{"categories":767},[210],{"categories":769},[218],{"categories":771},[223],{"categories":773},[210],{"categories":775},[210],{"categories":777},[237],{"categories":779},[310],{"categories":781},[210],{"categories":783},[223],{"categories":785},[],{"categories":787},[237],{"categories":789},[223],{"categories":791},[185],{"categories":793},[],{"categories":795},[210],{"categories":797},[259],{"categories":799},[210],{"categories":801},[],{"categories":803},[210],{"categories":805},[210],{"categories":807},[210],{"categories":809},[218,339],{"categories":811},[],{"categories":813},[237],{"categories":815},[210],{"categories":817},[210],{"categories":819},[223],{"categories":821},[210],{"categories":823},[],{"categories":825},[],{"categories":827},[210],{"categories":829},[310],{"categories":831},[210],{"categories":833},[],{"categories":835},[210],{"categories":837},[380],{"categories":839},[],{"categories":841},[223],{"categories":843},[259],{"categories":845},[210],{"categories":847},[210],{"categories":849},[310],{"categories":851},[],{"categories":853},[259],{"categories":855},[210],{"categories":857},[248],{"categories":859},[210],{"categories":861},[210],{"categories":863},[223],{"categories":865},[259],{"categories":867},[614],{"categories":869},[210],{"categories":871},[339],{"categories":873},[],{"categories":875},[223],{"categories":877},[218],{"categories":879},[237],{"categories":881},[210],{"categories":883},[223],{"categories":885},[],{"categories":887},[210,380],{"categories":889},[210],{"categories":891},[210],{"categories":893},[210],{"categories":895},[223],{"categories":897},[210,237],{"categories":899},[185],{"categories":901},[210],{"categories":903},[210],{"categories":905},[210],{"categories":907},[237],{"categories":909},[210],{"categories":911},[223],{"categories":913},[223],{"categories":915},[660],{"categories":917},[339],{"categories":919},[210],{"categories":921},[223],{"categories":923},[210],{"categories":925},[210],{"categories":927},[223],{"categories":929},[],{"categories":931},[223],{"categories":933},[210],{"categories":935},[210],{"categories":937},[223],{"categories":939},[210],{"categories":941},[210,218],{"categories":943},[210],{"categories":945},[218],{"categories":947},[],{"categories":949},[310],{"categories":951},[310],{"categories":953},[210],{"categories":955},[],{"categories":957},[],{"categories":959},[210],{"categories":961},[259],{"categories":963},[],{"categories":965},[213],{"categories":967},[210],{"categories":969},[237],{"categories":971},[210],{"categories":973},[974],"Generative UI & Design-to-Code",{"categories":976},[210],{"categories":978},[210],{"categories":980},[310],{"categories":982},[210],{"categories":984},[985],"Algorithmic Accountability",{"categories":987},[223],{"categories":989},[237],{"categories":991},[259],{"categories":993},[310],{"categories":995},[210],{"categories":997},[],{"categories":999},[226],{"categories":1001},[210],{"categories":1003},[210],{"categories":1005},[210],{"categories":1007},[210],{"categories":1009},[223],{"categories":1011},[1012],"MLOps & Infrastructure",{"categories":1014},[210],{"categories":1016},[210],{"categories":1018},[210],{"categories":1020},[210],{"categories":1022},[210],{"categories":1024},[237],{"categories":1026},[259],{"categories":1028},[210],{"categories":1030},[226],{"categories":1032},[213],{"categories":1034},[210],{"categories":1036},[223],{"categories":1038},[380],{"categories":1040},[210],{"categories":1042},[218],{"categories":1044},[210],{"categories":1046},[310],{"categories":1048},[210],{"categories":1050},[210],{"categories":1052},[223],{"categories":1054},[],{"categories":1056},[],{"categories":1058},[210],{"categories":1060},[248],{"categories":1062},[310],{"categories":1064},[259],{"categories":1066},[185],{"categories":1068},[],{"categories":1070},[210],{"categories":1072},[210],{"categories":1074},[218],{"categories":1076},[223],{"categories":1078},[210],{"categories":1080},[210],{"categories":1082},[210],{"categories":1084},[210],{"categories":1086},[259],{"categories":1088},[248],{"categories":1090},[210],{"categories":1092},[310],{"categories":1094},[210],{"categories":1096},[],{"categories":1098},[223],{"categories":1100},[237],{"categories":1102},[],{"categories":1104},[210],{"categories":1106},[210],{"categories":1108},[223],{"categories":1110},[237],{"categories":1112},[210],{"categories":1114},[185],{"categories":1116},[310],{"categories":1118},[],{"categories":1120},[210],{"categories":1122},[],{"categories":1124},[210],{"categories":1126},[],{"categories":1128},[210],{"categories":1130},[210],{"categories":1132},[226],{"categories":1134},[218],{"categories":1136},[223],{"categories":1138},[223],{"categories":1140},[],{"categories":1142},[210],{"categories":1144},[213],{"categories":1146},[210],{"categories":1148},[210],{"categories":1150},[218],{"categories":1152},[259],{"categories":1154},[213],{"categories":1156},[],{"categories":1158},[210],{"categories":1160},[],{"categories":1162},[210],{"categories":1164},[],{"categories":1166},[259],{"categories":1168},[259],{"categories":1170},[],{"categories":1172},[553],{"categories":1174},[210],{"categories":1176},[310],{"categories":1178},[237],{"categories":1180},[],{"categories":1182},[497],{"categories":1184},[223],{"categories":1186},[218],{"categories":1188},[],{"categories":1190},[],{"categories":1192},[213],{"categories":1194},[185],{"categories":1196},[],{"categories":1198},[339],{"categories":1200},[223],{"categories":1202},[218],{"categories":1204},[223],{"categories":1206},[210],{"categories":1208},[218],{"categories":1210},[210],{"categories":1212},[237],{"categories":1214},[],{"categories":1216},[248],{"categories":1218},[226],{"categories":1220},[210],{"categories":1222},[310],{"categories":1224},[237],{"categories":1226},[218],{"categories":1228},[210],{"categories":1230},[237],{"categories":1232},[210],{"categories":1234},[223],{"categories":1236},[218],{"categories":1238},[210],{"categories":1240},[210],{"categories":1242},[210],{"categories":1244},[210],{"categories":1246},[210],{"categories":1248},[],{"categories":1250},[],{"categories":1252},[237],{"categories":1254},[185],{"categories":1256},[226],{"categories":1258},[210],{"categories":1260},[223],{"categories":1262},[237],{"categories":1264},[237],{"categories":1266},[210],{"categories":1268},[],{"categories":1270},[259],{"categories":1272},[226],{"categories":1274},[226],{"categories":1276},[237],{"categories":1278},[210],{"categories":1280},[687],{"categories":1282},[380],{"categories":1284},[],{"categories":1286},[223],{"categories":1288},[210],{"categories":1290},[],{"categories":1292},[213],{"categories":1294},[],{"categories":1296},[210],{"categories":1298},[210],{"categories":1300},[210],{"categories":1302},[310],{"categories":1304},[339],{"categories":1306},[210],{"categories":1308},[237],{"categories":1310},[210],{"categories":1312},[223],{"categories":1314},[],{"categories":1316},[237],{"categories":1318},[210],{"categories":1320},[213],{"categories":1322},[],{"categories":1324},[218],{"categories":1326},[210],{"categories":1328},[210],{"categories":1330},[259],{"categories":1332},[210,380],{"categories":1334},[210],{"categories":1336},[1337],"Design Systems for AI",{"categories":1339},[210],{"categories":1341},[210],{"categories":1343},[259],{"categories":1345},[210],{"categories":1347},[210],{"categories":1349},[210],{"categories":1351},[218],{"categories":1353},[210],{"categories":1355},[210],{"categories":1357},[210],{"categories":1359},[],{"categories":1361},[210],{"categories":1363},[210],{"categories":1365},[218],{"categories":1367},[210],{"categories":1369},[],{"categories":1371},[223],{"categories":1373},[223],{"categories":1375},[237],{"categories":1377},[259],{"categories":1379},[237],{"categories":1381},[210],{"categories":1383},[310],{"categories":1385},[259],{"categories":1387},[185],{"categories":1389},[210],{"categories":1391},[210],{"categories":1393},[223],{"categories":1395},[213],{"categories":1397},[660],{"categories":1399},[210],{"categories":1401},[223],{"categories":1403},[210],{"categories":1405},[237],{"categories":1407},[237],{"categories":1409},[],{"categories":1411},[],{"categories":1413},[210],{"categories":1415},[223],{"categories":1417},[226],{"categories":1419},[],{"categories":1421},[218],{"categories":1423},[210],{"categories":1425},[],{"categories":1427},[310],{"categories":1429},[237],{"categories":1431},[223],{"categories":1433},[237],{"categories":1435},[310],{"categories":1437},[210],{"categories":1439},[210],{"categories":1441},[310],{"categories":1443},[],{"categories":1445},[],{"categories":1447},[259],{"categories":1449},[223],{"categories":1451},[223],{"categories":1453},[210],{"categories":1455},[210],{"categories":1457},[210],{"categories":1459},[210],{"categories":1461},[218],{"categories":1463},[210],{"categories":1465},[210],{"categories":1467},[],{"categories":1469},[237],{"categories":1471},[237],{"categories":1473},[210],{"categories":1475},[237],{"categories":1477},[218],{"categories":1479},[],{"categories":1481},[210],{"categories":1483},[210],{"categories":1485},[210],{"categories":1487},[210],{"categories":1489},[210],{"categories":1491},[223],{"categories":1493},[213],{"categories":1495},[218],{"categories":1497},[210],{"categories":1499},[223],{"categories":1501},[259],{"categories":1503},[223],{"categories":1505},[248],{"categories":1507},[339],{"categories":1509},[210],{"categories":1511},[223],{"categories":1513},[210],{"categories":1515},[210],{"categories":1517},[210],{"categories":1519},[],{"categories":1521},[310],{"categories":1523},[],{"categories":1525},[210],{"categories":1527},[210],{"categories":1529},[],{"categories":1531},[210],{"categories":1533},[237],{"categories":1535},[218],{"categories":1537},[1538],"Visual & Generative Media",{"categories":1540},[223],{"categories":1542},[],{"categories":1544},[210],{"categories":1546},[210],{"categories":1548},[237],{"categories":1550},[380],{"categories":1552},[210],{"categories":1554},[185],{"categories":1556},[660],{"categories":1558},[237],{"categories":1560},[339],{"categories":1562},[210],{"categories":1564},[310],{"categories":1566},[210],{"categories":1568},[210],{"categories":1570},[237],{"categories":1572},[223],{"categories":1574},[210],{"categories":1576},[],{"categories":1578},[],{"categories":1580},[223],{"categories":1582},[237],{"categories":1584},[213],{"categories":1586},[223],{"categories":1588},[614],{"categories":1590},[210],{"categories":1592},[226],{"categories":1594},[210],{"categories":1596},[218],{"categories":1598},[],{"categories":1600},[210],{"categories":1602},[226],{"categories":1604},[210],{"categories":1606},[210],{"categories":1608},[210],{"categories":1610},[226],{"categories":1612},[210],{"categories":1614},[210],{"categories":1616},[339],{"categories":1618},[210],{"categories":1620},[553],{"categories":1622},[210],{"categories":1624},[223],{"categories":1626},[210],{"categories":1628},[210],{"categories":1630},[223],{"categories":1632},[210],{"categories":1634},[210],{"categories":1636},[310],{"categories":1638},[223],{"categories":1640},[],{"categories":1642},[223],{"categories":1644},[],{"categories":1646},[380],{"categories":1648},[237],{"categories":1650},[],{"categories":1652},[614],{"categories":1654},[210],{"categories":1656},[223],{"categories":1658},[223],{"categories":1660},[210],{"categories":1662},[310,210],{"categories":1664},[213],{"categories":1666},[210],{"categories":1668},[310],{"categories":1670},[],{"categories":1672},[210],{"categories":1674},[213],{"categories":1676},[210],{"categories":1678},[1679],"Medical Imaging & Radiology",{"categories":1681},[210],{"categories":1683},[210],{"categories":1685},[210],{"categories":1687},[310],{"categories":1689},[223],{"categories":1691},[237],{"categories":1693},[],{"categories":1695},[210],{"categories":1697},[210],{"categories":1699},[210],{"categories":1701},[],{"categories":1703},[],{"categories":1705},[210],{"categories":1707},[210],{"categories":1709},[553],{"categories":1711},[210],{"categories":1713},[213],{"categories":1715},[210],{"categories":1717},[210],{"categories":1719},[],{"categories":1721},[223],{"categories":1723},[210],{"categories":1725},[226],{"categories":1727},[237],{"categories":1729},[210],{"categories":1731},[223],{"categories":1733},[553],{"categories":1735},[210],{"categories":1737},[223],{"categories":1739},[210],{"categories":1741},[210],{"categories":1743},[210],{"categories":1745},[310],{"categories":1747},[223],{"categories":1749},[380],{"categories":1751},[310],{"categories":1753},[218],{"categories":1755},[223],{"categories":1757},[259],{"categories":1759},[210],{"categories":1761},[210],{"categories":1763},[226],{"categories":1765},[210],{"categories":1767},[210],{"categories":1769},[210],{"categories":1771},[210],{"categories":1773},[223],{"categories":1775},[210],{"categories":1777},[237],{"categories":1779},[237],{"categories":1781},[210],{"categories":1783},[226],{"categories":1785},[],{"categories":1787},[259],{"categories":1789},[],{"categories":1791},[226],{"categories":1793},[223],{"categories":1795},[210],{"categories":1797},[223],{"categories":1799},[1337],{"categories":1801},[1337],{"categories":1803},[310],{"categories":1805},[210],{"categories":1807},[210],{"categories":1809},[210],{"categories":1811},[223],{"categories":1813},[237],{"categories":1815},[310],{"categories":1817},[223],{"categories":1819},[259],{"categories":1821},[],{"categories":1823},[210],{"categories":1825},[],{"categories":1827},[210],{"categories":1829},[210],{"categories":1831},[210],{"categories":1833},[210],{"categories":1835},[223],{"categories":1837},[1838],"Contract Review & E-Discovery",{"categories":1840},[210],{"categories":1842},[310],{"categories":1844},[210],{"categories":1846},[213],{"categories":1848},[210],{"categories":1850},[259],{"categories":1852},[210],{"categories":1854},[210],{"categories":1856},[339],{"categories":1858},[237],{"categories":1860},[210],{"categories":1862},[210],{"categories":1864},[223],{"categories":1866},[223],{"categories":1868},[985],{"categories":1870},[210],{"categories":1872},[210],{"categories":1874},[223],{"categories":1876},[223],{"categories":1878},[210],{"categories":1880},[210],{"categories":1882},[210],{"categories":1884},[223],{"categories":1886},[210],{"categories":1888},[210],{"categories":1890},[553],{"categories":1892},[530],{"categories":1894},[210],{"categories":1896},[223],{"categories":1898},[210],{"categories":1900},[1901],"Law-Firm Practice & Adoption",{"categories":1903},[210],{"categories":1905},[223],{"categories":1907},[310],{"categories":1909},[210],{"categories":1911},[210],{"categories":1913},[210],{"categories":1915},[],{"categories":1917},[237],{"categories":1919},[],{"categories":1921},[237],{"categories":1923},[210],{"categories":1925},[],{"categories":1927},[223],{"categories":1929},[213],{"categories":1931},[380],{"categories":1933},[210],{"categories":1935},[],{"categories":1937},[213],{"categories":1939},[218],{"categories":1941},[210],{"categories":1943},[339],{"categories":1945},[],{"categories":1947},[218],{"categories":1949},[223],{"categories":1951},[218],{"categories":1953},[],{"categories":1955},[210],{"categories":1957},[226],{"categories":1959},[210],{"categories":1961},[237],{"categories":1963},[],{"categories":1965},[],{"categories":1967},[],{"categories":1969},[],{"categories":1971},[210],{"categories":1973},[226],{"categories":1975},[223],{"categories":1977},[380],{"categories":1979},[210],{"categories":1981},[213],{"categories":1983},[237],{"categories":1985},[210],{"categories":1987},[210],{"categories":1989},[237],{"categories":1991},[226],{"categories":1993},[210],{"categories":1995},[210],{"categories":1997},[210],{"categories":1999},[1012],{"categories":2001},[210],{"categories":2003},[237],{"categories":2005},[210],{"categories":2007},[339],{"categories":2009},[237],{"categories":2011},[218],{"categories":2013},[210],{"categories":2015},[210],{"categories":2017},[210],{"categories":2019},[310],{"categories":2021},[210],{"categories":2023},[210],{"categories":2025},[210],{"categories":2027},[210],{"categories":2029},[218],{"categories":2031},[223],{"categories":2033},[210,213],{"categories":2035},[553],{"categories":2037},[210],{"categories":2039},[210],{"categories":2041},[237],{"categories":2043},[237],{"categories":2045},[310],{"categories":2047},[223],{"categories":2049},[223],{"categories":2051},[237],{"categories":2053},[210],{"categories":2055},[210],{"categories":2057},[210],{"categories":2059},[],{"categories":2061},[],{"categories":2063},[210],{"categories":2065},[185],{"categories":2067},[210],{"categories":2069},[310],{"categories":2071},[223],{"categories":2073},[],{"categories":2075},[210],{"categories":2077},[210],{"categories":2079},[237],{"categories":2081},[185],{"categories":2083},[259],{"categories":2085},[310],{"categories":2087},[210],{"categories":2089},[223],{"categories":2091},[210],{"categories":2093},[237],{"categories":2095},[],{"categories":2097},[223],{"categories":2099},[210],{"categories":2101},[210],{"categories":2103},[210],{"categories":2105},[210],{"categories":2107},[],{"categories":2109},[223],{"categories":2111},[210],{"categories":2113},[210],{"categories":2115},[210],{"categories":2117},[],{"categories":2119},[223],{"categories":2121},[210],{"categories":2123},[210],{"categories":2125},[218],{"categories":2127},[210],{"categories":2129},[210],{"categories":2131},[],{"categories":2133},[213],{"categories":2135},[210],{"categories":2137},[210],{"categories":2139},[210],{"categories":2141},[310],{"categories":2143},[210],{"categories":2145},[237],{"categories":2147},[210],{"categories":2149},[213],{"categories":2151},[210],{"categories":2153},[237],{"categories":2155},[339],{"categories":2157},[223],{"categories":2159},[223],{"categories":2161},[210],{"categories":2163},[210],{"categories":2165},[210,310],{"categories":2167},[210],{"categories":2169},[223],{"categories":2171},[259],{"categories":2173},[210],{"categories":2175},[259],{"categories":2177},[223],{"categories":2179},[310],{"categories":2181},[210],{"categories":2183},[],{"categories":2185},[237],{"categories":2187},[380],{"categories":2189},[310],{"categories":2191},[237],{"categories":2193},[210],{"categories":2195},[226],{"categories":2197},[210],{"categories":2199},[210],{"categories":2201},[223],{"categories":2203},[],{"categories":2205},[],{"categories":2207},[210],{"categories":2209},[],{"categories":2211},[],{"categories":2213},[226],{"categories":2215},[237],{"categories":2217},[210],{"categories":2219},[223],{"categories":2221},[223],{"categories":2223},[218],{"categories":2225},[223],{"categories":2227},[380],{"categories":2229},[210],{"categories":2231},[210],{"categories":2233},[210],{"categories":2235},[248],{"categories":2237},[210],{"categories":2239},[210],{"categories":2241},[210],{"categories":2243},[237],{"categories":2245},[223],{"categories":2247},[210],{"categories":2249},[210],{"categories":2251},[237],{"categories":2253},[497],{"categories":2255},[223],{"categories":2257},[985],{"categories":2259},[],{"categories":2261},[310],{"categories":2263},[1901],{"categories":2265},[237],{"categories":2267},[],{"categories":2269},[],{"categories":2271},[210],{"categories":2273},[223],{"categories":2275},[],{"categories":2277},[],{"categories":2279},[210],{"categories":2281},[339],{"categories":2283},[210],{"categories":2285},[339],{"categories":2287},[223],{"categories":2289},[210],{"categories":2291},[210],{"categories":2293},[237],{"categories":2295},[226],{"categories":2297},[],{"categories":2299},[210],{"categories":2301},[210],{"categories":2303},[237],{"categories":2305},[1838],{"categories":2307},[310],{"categories":2309},[310],{"categories":2311},[210],{"categories":2313},[223],{"categories":2315},[213],{"categories":2317},[210],{"categories":2319},[210],{"categories":2321},[210],{"categories":2323},[210],{"categories":2325},[310],{"categories":2327},[310],{"categories":2329},[223],{"categories":2331},[223],{"categories":2333},[223],{"categories":2335},[210],{"categories":2337},[210],{"categories":2339},[],{"categories":2341},[210],{"categories":2343},[],{"categories":2345},[2346],"Interaction & Product Design",{"categories":2348},[210],{"categories":2350},[223],{"categories":2352},[237],{"categories":2354},[409],{"categories":2356},[259],{"categories":2358},[237],{"categories":2360},[210],{"categories":2362},[210],{"categories":2364},[210],{"categories":2366},[237],{"categories":2368},[210],{"categories":2370},[213],{"categories":2372},[223],{"categories":2374},[210],{"categories":2376},[],{"categories":2378},[223],{"categories":2380},[223],{"categories":2382},[223],{"categories":2384},[],{"categories":2386},[237],{"categories":2388},[210],{"categories":2390},[223],{"categories":2392},[213],{"categories":2394},[2346],{"categories":2396},[210],{"categories":2398},[213],{"categories":2400},[213],{"categories":2402},[],{"categories":2404},[223],{"categories":2406},[237],{"categories":2408},[],{"categories":2410},[223],{"categories":2412},[259],{"categories":2414},[210],{"categories":2416},[223],{"categories":2418},[210],{"categories":2420},[223],{"categories":2422},[223],{"categories":2424},[210],{"categories":2426},[210],{"categories":2428},[259],{"categories":2430},[185],{"categories":2432},[210],{"categories":2434},[226],{"categories":2436},[237],{"categories":2438},[2439],"Coding Agents & Dev Productivity",{"categories":2441},[259],{"categories":2443},[310],{"categories":2445},[210],{"categories":2447},[210],{"categories":2449},[],{"categories":2451},[210],{"categories":2453},[985],{"categories":2455},[],{"categories":2457},[210],{"categories":2459},[210],{"categories":2461},[380],{"categories":2463},[210],{"categories":2465},[259],{"categories":2467},[],{"categories":2469},[],{"categories":2471},[210],{"categories":2473},[],{"categories":2475},[223],{"categories":2477},[210],{"categories":2479},[],{"categories":2481},[237],{"categories":2483},[237],{"categories":2485},[210],{"categories":2487},[185],{"categories":2489},[],{"categories":2491},[210],{"categories":2493},[210],{"categories":2495},[210],{"categories":2497},[185],{"categories":2499},[237],{"categories":2501},[223],{"categories":2503},[],{"categories":2505},[],{"categories":2507},[210],{"categories":2509},[210],{"categories":2511},[223],{"categories":2513},[223],{"categories":2515},[474],{"categories":2517},[237],{"categories":2519},[226],{"categories":2521},[237],{"categories":2523},[223],{"categories":2525},[259],{"categories":2527},[259],{"categories":2529},[223],{"categories":2531},[223],{"categories":2533},[210],{"categories":2535},[213],{"categories":2537},[2346],{"categories":2539},[226],{"categories":2541},[210,380],{"categories":2543},[185],{"categories":2545},[],{"categories":2547},[310],{"categories":2549},[223],{"categories":2551},[237],{"categories":2553},[213],{"categories":2555},[210],{"categories":2557},[223],{"categories":2559},[2560],"The Designer's Role & Craft",{"categories":2562},[310],{"categories":2564},[],{"categories":2566},[223],{"categories":2568},[210],{"categories":2570},[223],{"categories":2572},[223],{"categories":2574},[210],{"categories":2576},[339],{"categories":2578},[210],{"categories":2580},[237],{"categories":2582},[210],{"categories":2584},[310],{"categories":2586},[210],{"categories":2588},[],{"categories":2590},[223],{"categories":2592},[310],{"categories":2594},[226],{"categories":2596},[210],{"categories":2598},[210],{"categories":2600},[210],{"categories":2602},[2603],"AI UX Patterns",{"categories":2605},[223],{"categories":2607},[223],{"categories":2609},[223],{"categories":2611},[223],{"categories":2613},[339],{"categories":2615},[185],{"categories":2617},[210],{"categories":2619},[223],{"categories":2621},[210],{"categories":2623},[1337],{"categories":2625},[],{"categories":2627},[339],{"categories":2629},[223],{"categories":2631},[259],{"categories":2633},[237],{"categories":2635},[210],{"categories":2637},[223],{"categories":2639},[],{"categories":2641},[],{"categories":2643},[210],{"categories":2645},[210],{"categories":2647},[223],{"categories":2649},[210],{"categories":2651},[223],{"categories":2653},[474],{"categories":2655},[310],{"categories":2657},[210],{"categories":2659},[259],{"categories":2661},[237],{"categories":2663},[210],{"categories":2665},[223],{"categories":2667},[223],{"categories":2669},[],{"categories":2671},[210],{"categories":2673},[],{"categories":2675},[210],{"categories":2677},[],{"categories":2679},[210],{"categories":2681},[210],{"categories":2683},[210],{"categories":2685},[223],{"categories":2687},[237],{"categories":2689},[],{"categories":2691},[],{"categories":2693},[185],{"categories":2695},[248],{"categories":2697},[210],{"categories":2699},[210],{"categories":2701},[210],{"categories":2703},[185],{"categories":2705},[210],{"categories":2707},[210],{"categories":2709},[259],{"categories":2711},[210],{"categories":2713},[210],{"categories":2715},[210],{"categories":2717},[223],{"categories":2719},[210],{"categories":2721},[223],{"categories":2723},[210],{"categories":2725},[210],{"categories":2727},[210],{"categories":2729},[223],{"categories":2731},[],{"categories":2733},[210],{"categories":2735},[],{"categories":2737},[210],{"categories":2739},[210],{"categories":2741},[380],{"categories":2743},[210],{"categories":2745},[],{"categories":2747},[],{"categories":2749},[310],{"categories":2751},[1012],{"categories":2753},[223],{"categories":2755},[213],{"categories":2757},[2560],{"categories":2759},[],{"categories":2761},[],{"categories":2763},[210],{"categories":2765},[],{"categories":2767},[],{"categories":2769},[237],{"categories":2771},[259],{"categories":2773},[339],{"categories":2775},[223],{"categories":2777},[218],{"categories":2779},[210],{"categories":2781},[210],{"categories":2783},[218],{"categories":2785},[],{"categories":2787},[310],{"categories":2789},[226],{"categories":2791},[210],{"categories":2793},[210],{"categories":2795},[223],{"categories":2797},[218],{"categories":2799},[210],{"categories":2801},[210],{"categories":2803},[213],{"categories":2805},[210],{"categories":2807},[210],{"categories":2809},[],{"categories":2811},[213],{"categories":2813},[210],{"categories":2815},[339],{"categories":2817},[223],{"categories":2819},[259],{"categories":2821},[210],{"categories":2823},[237],{"categories":2825},[210],{"categories":2827},[210],{"categories":2829},[218],{"categories":2831},[210],{"categories":2833},[210],{"categories":2835},[210],{"categories":2837},[223],{"categories":2839},[210],{"categories":2841},[],{"categories":2843},[210],{"categories":2845},[237],{"categories":2847},[213],{"categories":2849},[210],{"categories":2851},[210],{"categories":2853},[210],{"categories":2855},[],{"categories":2857},[210],{"categories":2859},[553],{"categories":2861},[223],{"categories":2863},[218],{"categories":2865},[259],{"categories":2867},[210],{"categories":2869},[210],{"categories":2871},[],{"categories":2873},[218],{"categories":2875},[218],{"categories":2877},[210],{"categories":2879},[210],{"categories":2881},[226],{"categories":2883},[210],{"categories":2885},[210],{"categories":2887},[210],{"categories":2889},[210],{"categories":2891},[237],{"categories":2893},[237],{"categories":2895},[210],{"categories":2897},[],{"categories":2899},[237],{"categories":2901},[210],{"categories":2903},[237],{"categories":2905},[223],{"categories":2907},[660],{"categories":2909},[],{"categories":2911},[],{"categories":2913},[210],{"categories":2915},[259],{"categories":2917},[],{"categories":2919},[380],{"categories":2921},[210],{"categories":2923},[210],{"categories":2925},[210],{"categories":2927},[310],{"categories":2929},[974],{"categories":2931},[],{"categories":2933},[210],{"categories":2935},[210],{"categories":2937},[210],{"categories":2939},[237],{"categories":2941},[210],{"categories":2943},[210],{"categories":2945},[210,380],{"categories":2947},[210],{"categories":2949},[210],{"categories":2951},[310],{"categories":2953},[223],{"categories":2955},[],{"categories":2957},[223],{"categories":2959},[223],{"categories":2961},[210],{"categories":2963},[210],{"categories":2965},[210],{"categories":2967},[210],{"categories":2969},[185],{"categories":2971},[210],{"categories":2973},[2603],{"categories":2975},[213],{"categories":2977},[185],{"categories":2979},[213],{"categories":2981},[237],{"categories":2983},[310],{"categories":2985},[223],{"categories":2987},[210],{"categories":2989},[],{"categories":2991},[218],{"categories":2993},[210],{"categories":2995},[210],{"categories":2997},[259],{"categories":2999},[210],{"categories":3001},[210],{"categories":3003},[210],{"categories":3005},[223],{"categories":3007},[210],{"categories":3009},[210],{"categories":3011},[210],{"categories":3013},[218],{"categories":3015},[],{"categories":3017},[380],{"categories":3019},[210],{"categories":3021},[474],{"categories":3023},[310],{"categories":3025},[310],{"categories":3027},[237],{"categories":3029},[223],{"categories":3031},[210],{"categories":3033},[218],{"categories":3035},[259],{"categories":3037},[210],{"categories":3039},[210],{"categories":3041},[210],{"categories":3043},[310],{"categories":3045},[223],{"categories":3047},[223],{"categories":3049},[210],{"categories":3051},[210],{"categories":3053},[614],{"categories":3055},[223],{"categories":3057},[],{"categories":3059},[210],{"categories":3061},[210],{"categories":3063},[210],{"categories":3065},[],{"categories":3067},[],{"categories":3069},[210],{"categories":3071},[210],{"categories":3073},[223],{"categories":3075},[210],{"categories":3077},[210],{"categories":3079},[210],{"categories":3081},[237],{"categories":3083},[210],{"categories":3085},[210],{"categories":3087},[223],{"categories":3089},[210],{"categories":3091},[210],{"categories":3093},[210],{"categories":3095},[210],{"categories":3097},[210],{"categories":3099},[],{"categories":3101},[237],{"categories":3103},[185],{"categories":3105},[210],{"categories":3107},[223],{"categories":3109},[223],{"categories":3111},[210],{"categories":3113},[210],{"categories":3115},[],{"categories":3117},[],{"categories":3119},[210],{"categories":3121},[210],{"categories":3123},[210],{"categories":3125},[259],{"categories":3127},[185],{"categories":3129},[],{"categories":3131},[210],{"categories":3133},[310],{"categories":3135},[210],{"categories":3137},[380],{"categories":3139},[1901],{"categories":3141},[259],{"categories":3143},[237],{"categories":3145},[210],{"categories":3147},[237],{"categories":3149},[237],{"categories":3151},[210],{"categories":3153},[210],{"categories":3155},[237],{"categories":3157},[259],{"categories":3159},[259],{"categories":3161},[380],{"categories":3163},[223],{"categories":3165},[],{"categories":3167},[259],{"categories":3169},[210],{"categories":3171},[223],{"categories":3173},[213],{"categories":3175},[237],{"categories":3177},[210],{"categories":3179},[259],{"categories":3181},[],{"categories":3183},[210],{"categories":3185},[237],{"categories":3187},[237],{"categories":3189},[185],{"categories":3191},[210],{"categories":3193},[259],{"categories":3195},[210],{"categories":3197},[237],{"categories":3199},[223],{"categories":3201},[223],{"categories":3203},[259],{"categories":3205},[223],{"categories":3207},[380],{"categories":3209},[223],{"categories":3211},[210],{"categories":3213},[210],{"categories":3215},[210],{"categories":3217},[210],{"categories":3219},[237],{"categories":3221},[210],{"categories":3223},[],{"categories":3225},[223],{"categories":3227},[218],{"categories":3229},[237],{"categories":3231},[],{"categories":3233},[],{"categories":3235},[210],{"categories":3237},[223],{"categories":3239},[210],{"categories":3241},[210],{"categories":3243},[3244],"Frameworks & Tooling",{"categories":3246},[210],{"categories":3248},[210],{"categories":3250},[237],{"categories":3252},[210],{"categories":3254},[210],{"categories":3256},[],{"categories":3258},[185],{"categories":3260},[185],{"categories":3262},[213],{"categories":3264},[210],{"categories":3266},[223],{"categories":3268},[210],{"categories":3270},[310],{"categories":3272},[],{"categories":3274},[1901],{"categories":3276},[210],{"categories":3278},[237],{"categories":3280},[210],{"categories":3282},[380],{"categories":3284},[380],{"categories":3286},[],{"categories":3288},[223],{"categories":3290},[223],{"categories":3292},[210],{"categories":3294},[210],{"categories":3296},[259],{"categories":3298},[223],{"categories":3300},[259],{"categories":3302},[210],{"categories":3304},[223],{"categories":3306},[],{"categories":3308},[310],{"categories":3310},[210],{"categories":3312},[210],{"categories":3314},[],{"categories":3316},[210],{"categories":3318},[223],{"categories":3320},[210],{"categories":3322},[210],{"categories":3324},[210],{"categories":3326},[],{"categories":3328},[218],{"categories":3330},[237],{"categories":3332},[210],{"categories":3334},[237],{"categories":3336},[380],{"categories":3338},[210],{"categories":3340},[210],{"categories":3342},[210],{"categories":3344},[237],{"categories":3346},[218],{"categories":3348},[210],{"categories":3350},[1901],{"categories":3352},[],{"categories":3354},[223],{"categories":3356},[213],{"categories":3358},[210],{"categories":3360},[213],{"categories":3362},[210],{"categories":3364},[],{"categories":3366},[223],{"categories":3368},[210],{"categories":3370},[210],{"categories":3372},[3373],"AI Design Tooling",{"categories":3375},[310],{"categories":3377},[210],{"categories":3379},[210],{"categories":3381},[237],{"categories":3383},[310],{"categories":3385},[210],{"categories":3387},[210],{"categories":3389},[237],{"categories":3391},[259],{"categories":3393},[226],{"categories":3395},[237],{"categories":3397},[210],{"categories":3399},[210],{"categories":3401},[210],{"categories":3403},[223],{"categories":3405},[210],{"categories":3407},[],{"categories":3409},[223],{"categories":3411},[210],{"categories":3413},[210],{"categories":3415},[223],{"categories":3417},[210],{"categories":3419},[210],{"categories":3421},[210],{"categories":3423},[223],{"categories":3425},[],{"categories":3427},[223],{"categories":3429},[3244],{"categories":3431},[210],{"categories":3433},[210],{"categories":3435},[223],{"categories":3437},[223],{"categories":3439},[237],{"categories":3441},[237],{"categories":3443},[210],{"categories":3445},[],{"categories":3447},[237],{"categories":3449},[210],{"categories":3451},[210],{"categories":3453},[223],{"categories":3455},[218],{"categories":3457},[210],{"categories":3459},[],{"categories":3461},[210],{"categories":3463},[210],{"categories":3465},[2346],{"categories":3467},[],{"categories":3469},[210],{"categories":3471},[210],{"categories":3473},[210],{"categories":3475},[210],{"categories":3477},[310],{"categories":3479},[210],{"categories":3481},[],{"categories":3483},[210],{"categories":3485},[210],{"categories":3487},[210],{"categories":3489},[210],{"categories":3491},[339],{"categories":3493},[259],{"categories":3495},[210],{"categories":3497},[210],{"categories":3499},[1901],{"categories":3501},[213],{"categories":3503},[210],{"categories":3505},[210],{"categories":3507},[185],{"categories":3509},[210],{"categories":3511},[210],{"categories":3513},[259],{"categories":3515},[223],{"categories":3517},[],{"categories":3519},[210],{"categories":3521},[210],{"categories":3523},[310],{"categories":3525},[210],{"categories":3527},[339],{"categories":3529},[223],{"categories":3531},[210],{"categories":3533},[223],{"categories":3535},[],{"categories":3537},[],{"categories":3539},[],{"categories":3541},[213],{"categories":3543},[259],{"categories":3545},[223],{"categories":3547},[210],{"categories":3549},[210],{"categories":3551},[210],{"categories":3553},[210],{"categories":3555},[497],{"categories":3557},[310],{"categories":3559},[223],{"categories":3561},[210],{"categories":3563},[],{"categories":3565},[223],{"categories":3567},[223],{"categories":3569},[],{"categories":3571},[210],{"categories":3573},[223],{"categories":3575},[210],{"categories":3577},[],{"categories":3579},[210],{"categories":3581},[210],{"categories":3583},[210],{"categories":3585},[259],{"categories":3587},[310],{"categories":3589},[223],{"categories":3591},[310],{"categories":3593},[223],{"categories":3595},[210],{"categories":3597},[218],{"categories":3599},[],{"categories":3601},[],{"categories":3603},[210],{"categories":3605},[210],{"categories":3607},[210],{"categories":3609},[213],{"categories":3611},[223],{"categories":3613},[259],{"categories":3615},[],{"categories":3617},[310],{"categories":3619},[],{"categories":3621},[237],{"categories":3623},[210],{"categories":3625},[237],{"categories":3627},[310],{"categories":3629},[237],{"categories":3631},[210],{"categories":3633},[],{"categories":3635},[210],{"categories":3637},[210],{"categories":3639},[],{"categories":3641},[210],{"categories":3643},[210],{"categories":3645},[339],{"categories":3647},[210],{"categories":3649},[210],{"categories":3651},[380],{"categories":3653},[237],{"categories":3655},[210],{"categories":3657},[],{"categories":3659},[223],{"categories":3661},[210],{"categories":3663},[213],{"categories":3665},[614],{"categories":3667},[210],{"categories":3669},[210],{"categories":3671},[223],{"categories":3673},[210],{"categories":3675},[223],{"categories":3677},[210],{"categories":3679},[210],{"categories":3681},[210],{"categories":3683},[210],{"categories":3685},[],{"categories":3687},[210],{"categories":3689},[213],{"categories":3691},[210],{"categories":3693},[218],{"categories":3695},[237],{"categories":3697},[310],{"categories":3699},[],{"categories":3701},[210],{"categories":3703},[],{"categories":3705},[223],{"categories":3707},[210],{"categories":3709},[],{"categories":3711},[223],{"categories":3713},[210],{"categories":3715},[237],{"categories":3717},[310],{"categories":3719},[259],{"categories":3721},[210],{"categories":3723},[259],{"categories":3725},[223],{"categories":3727},[310],{"categories":3729},[210],{"categories":3731},[],{"categories":3733},[210],{"categories":3735},[248],{"categories":3737},[223],{"categories":3739},[210],{"categories":3741},[310],{"categories":3743},[259],{"categories":3745},[218],{"categories":3747},[237],{"categories":3749},[210],{"categories":3751},[210],{"categories":3753},[210],{"categories":3755},[210],{"categories":3757},[259],{"categories":3759},[339],{"categories":3761},[],{"categories":3763},[],{"categories":3765},[185],{"categories":3767},[553],{"categories":3769},[210],{"categories":3771},[223],{"categories":3773},[210,237],{"categories":3775},[259],{"categories":3777},[210],{"categories":3779},[210],{"categories":3781},[210],{"categories":3783},[210],{"categories":3785},[210],{"categories":3787},[210],{"categories":3789},[210],{"categories":3791},[223],{"categories":3793},[210],{"categories":3795},[223],{"categories":3797},[210],{"categories":3799},[210],{"categories":3801},[210],{"categories":3803},[],{"categories":3805},[210],{"categories":3807},[1337],{"categories":3809},[237],{"categories":3811},[310],{"categories":3813},[210],{"categories":3815},[210],{"categories":3817},[210],{"categories":3819},[185],{"categories":3821},[223],{"categories":3823},[339],{"categories":3825},[380],{"categories":3827},[],{"categories":3829},[237],{"categories":3831},[210],{"categories":3833},[218],{"categories":3835},[223],{"categories":3837},[213],{"categories":3839},[223],{"categories":3841},[210],{"categories":3843},[223],{"categories":3845},[223],{"categories":3847},[226],{"categories":3849},[237],{"categories":3851},[210],{"categories":3853},[210],{"categories":3855},[],{"categories":3857},[],{"categories":3859},[],{"categories":3861},[380],{"categories":3863},[210],{"categories":3865},[259],{"categories":3867},[210],{"categories":3869},[210],{"categories":3871},[210],{"categories":3873},[210],{"categories":3875},[],{"categories":3877},[210],{"categories":3879},[185],{"categories":3881},[218],{"categories":3883},[223],{"categories":3885},[210],{"categories":3887},[],{"categories":3889},[210],{"categories":3891},[223],{"categories":3893},[237],{"categories":3895},[210],{"categories":3897},[380],{"categories":3899},[],{"categories":3901},[310],{"categories":3903},[310],{"categories":3905},[210],{"categories":3907},[223],{"categories":3909},[],{"categories":3911},[237],{"categories":3913},[210],{"categories":3915},[310],{"categories":3917},[210],{"categories":3919},[218],{"categories":3921},[223],{"categories":3923},[210],{"categories":3925},[],{"categories":3927},[259],{"categories":3929},[210],{"categories":3931},[210],{"categories":3933},[210],{"categories":3935},[310],{"categories":3937},[223],{"categories":3939},[259],{"categories":3941},[],{"categories":3943},[223],{"categories":3945},[218],{"categories":3947},[223],{"categories":3949},[310],{"categories":3951},[210],{"categories":3953},[210],{"categories":3955},[210],{"categories":3957},[553],{"categories":3959},[210],{"categories":3961},[223],{"categories":3963},[],{"categories":3965},[210],{"categories":3967},[210],{"categories":3969},[380],{"categories":3971},[259],{"categories":3973},[185],{"categories":3975},[660],{"categories":3977},[185],{"categories":3979},[185],{"categories":3981},[210],{"categories":3983},[],{"categories":3985},[],{"categories":3987},[],{"categories":3989},[223],{"categories":3991},[210],{"categories":3993},[223],{"categories":3995},[223],{"categories":3997},[237],{"categories":3999},[210],{"categories":4001},[530],{"categories":4003},[237],{"categories":4005},[223],{"categories":4007},[210],{"categories":4009},[210],{"categories":4011},[210],{"categories":4013},[210],{"categories":4015},[210],{"categories":4017},[223],{"categories":4019},[210],{"categories":4021},[],{"categories":4023},[],{"categories":4025},[210],{"categories":4027},[],{"categories":4029},[210],{"categories":4031},[223],{"categories":4033},[310],{"categories":4035},[210],{"categories":4037},[210],{"categories":4039},[],{"categories":4041},[223],{"categories":4043},[210],{"categories":4045},[210],{"categories":4047},[226],{"categories":4049},[210],{"categories":4051},[310],{"categories":4053},[210],{"categories":4055},[223],{"categories":4057},[218],{"categories":4059},[210],{"categories":4061},[210],{"categories":4063},[339],{"categories":4065},[223],{"categories":4067},[210],{"categories":4069},[210],{"categories":4071},[974],{"categories":4073},[210],{"categories":4075},[223],{"categories":4077},[210],{"categories":4079},[237],{"categories":4081},[210],{"categories":4083},[614],{"categories":4085},[310],{"categories":4087},[],{"categories":4089},[210],{"categories":4091},[210],{"categories":4093},[259],{"categories":4095},[553],{"categories":4097},[223],{"categories":4099},[210],{"categories":4101},[],{"categories":4103},[259],{"categories":4105},[474],{"categories":4107},[223],{"categories":4109},[223],{"categories":4111},[223],{"categories":4113},[210],{"categories":4115},[210],{"categories":4117},[223],{"categories":4119},[],{"categories":4121},[218],{"categories":4123},[210],{"categories":4125},[218],{"categories":4127},[223],{"categories":4129},[],{"categories":4131},[237],{"categories":4133},[210],{"categories":4135},[210],{"categories":4137},[213],{"categories":4139},[210],{"categories":4141},[259],{"categories":4143},[380],{"categories":4145},[248],{"categories":4147},[223],{"categories":4149},[223],{"categories":4151},[210],{"categories":4153},[210],{"categories":4155},[223],{"categories":4157},[210],{"categories":4159},[213],{"categories":4161},[],{"categories":4163},[223],{"categories":4165},[210],{"categories":4167},[210],{"categories":4169},[210],{"categories":4171},[223],{"categories":4173},[210],{"categories":4175},[],{"categories":4177},[210],{"categories":4179},[],{"categories":4181},[310],{"categories":4183},[223],{"categories":4185},[210,218],{"categories":4187},[223],{"categories":4189},[210],{"categories":4191},[],{"categories":4193},[213],{"categories":4195},[185],{"categories":4197},[218],{"categories":4199},[210],{"categories":4201},[237],{"categories":4203},[210],{"categories":4205},[210],{"categories":4207},[223],{"categories":4209},[210],{"categories":4211},[210],{"categories":4213},[210],{"categories":4215},[259],{"categories":4217},[1337],{"categories":4219},[223],{"categories":4221},[210],{"categories":4223},[],{"categories":4225},[],{"categories":4227},[210],{"categories":4229},[223],{"categories":4231},[210],{"categories":4233},[210],{"categories":4235},[380],{"categories":4237},[],{"categories":4239},[210],{"categories":4241},[223],{"categories":4243},[248],{"categories":4245},[223],{"categories":4247},[553],{"categories":4249},[],{"categories":4251},[497],{"categories":4253},[223],{"categories":4255},[210],{"categories":4257},[210],{"categories":4259},[339],{"categories":4261},[223],{"categories":4263},[210],{"categories":4265},[185],{"categories":4267},[226],{"categories":4269},[223],{"categories":4271},[210],{"categories":4273},[553],{"categories":4275},[210],{"categories":4277},[380],{"categories":4279},[218],{"categories":4281},[],{"categories":4283},[210],{"categories":4285},[210],{"categories":4287},[339],{"categories":4289},[310],{"categories":4291},[210],{"categories":4293},[210],{"categories":4295},[210],{"categories":4297},[],{"categories":4299},[339],{"categories":4301},[259],{"categories":4303},[210],{"categories":4305},[210],{"categories":4307},[210],{"categories":4309},[660],{"categories":4311},[213],{"categories":4313},[210],{"categories":4315},[226],{"categories":4317},[210],{"categories":4319},[],{"categories":4321},[],{"categories":4323},[310],{"categories":4325},[210],{"categories":4327},[185],{"categories":4329},[339],{"categories":4331},[223],{"categories":4333},[210],{"categories":4335},[210],{"categories":4337},[339],{"categories":4339},[259],{"categories":4341},[210],{"categories":4343},[],{"categories":4345},[210],{"categories":4347},[210],{"categories":4349},[],{"categories":4351},[210],{"categories":4353},[210],{"categories":4355},[687],{"categories":4357},[210],{"categories":4359},[210],{"categories":4361},[223],{"categories":4363},[237],{"categories":4365},[553],{"categories":4367},[210],{"categories":4369},[210],{"categories":4371},[210],{"categories":4373},[],{"categories":4375},[210,237],{"categories":4377},[259],{"categories":4379},[223],{"categories":4381},[237],{"categories":4383},[223],{"categories":4385},[1012],{"categories":4387},[237],{"categories":4389},[237],{"categories":4391},[223],{"categories":4393},[210],{"categories":4395},[213],{"categories":4397},[],{"categories":4399},[],{"categories":4401},[223],{"categories":4403},[210],{"categories":4405},[237],{"categories":4407},[210],{"categories":4409},[213],{"categories":4411},[237],{"categories":4413},[237],{"categories":4415},[210],{"categories":4417},[339],{"categories":4419},[210],{"categories":4421},[237],{"categories":4423},[210],{"categories":4425},[],{"categories":4427},[210],{"categories":4429},[210],{"categories":4431},[310,210],{"categories":4433},[380],{"categories":4435},[213],{"categories":4437},[210],{"categories":4439},[],{"categories":4441},[210],{"categories":4443},[210],{"categories":4445},[218],{"categories":4447},[210],{"categories":4449},[218],{"categories":4451},[210],{"categories":4453},[210],{"categories":4455},[474],{"categories":4457},[210],{"categories":4459},[218],{"categories":4461},[237],{"categories":4463},[185],{"categories":4465},[223],{"categories":4467},[210],{"categories":4469},[237],{"categories":4471},[210],{"categories":4473},[210],{"categories":4475},[259],{"categories":4477},[339],{"categories":4479},[310],{"categories":4481},[210],{"categories":4483},[210],{"categories":4485},[210],{"categories":4487},[210],{"categories":4489},[213],{"categories":4491},[210],{"categories":4493},[223],{"categories":4495},[223],{"categories":4497},[237],{"categories":4499},[259],{"categories":4501},[237],{"categories":4503},[237],{"categories":4505},[210],{"categories":4507},[210],{"categories":4509},[],{"categories":4511},[],{"categories":4513},[185],{"categories":4515},[210],{"categories":4517},[237],{"categories":4519},[210],{"categories":4521},[310],{"categories":4523},[553],{"categories":4525},[497],{"categories":4527},[474],{"categories":4529},[210],{"categories":4531},[210],{"categories":4533},[210],{"categories":4535},[185],{"categories":4537},[210],{"categories":4539},[210],{"categories":4541},[210],{"categories":4543},[210],{"categories":4545},[210],{"categories":4547},[210],{"categories":4549},[210],{"categories":4551},[223],{"categories":4553},[213],{"categories":4555},[223],{"categories":4557},[210,218],{"categories":4559},[],{"categories":4561},[310],{"categories":4563},[],{"categories":4565},[226],{"categories":4567},[210],{"categories":4569},[259],{"categories":4571},[213],{"categories":4573},[210],{"categories":4575},[213],{"categories":4577},[223],{"categories":4579},[185],{"categories":4581},[223],{"categories":4583},[226],{"categories":4585},[223],{"categories":4587},[210],{"categories":4589},[210],{"categories":4591},[210],{"categories":4593},[218],{"categories":4595},[223],{"categories":4597},[237],{"categories":4599},[339],{"categories":4601},[210],{"categories":4603},[210],{"categories":4605},[],{"categories":4607},[259],{"categories":4609},[210],{"categories":4611},[210],{"categories":4613},[210],{"categories":4615},[210],{"categories":4617},[210],{"categories":4619},[210],{"categories":4621},[237],{"categories":4623},[259],{"categories":4625},[237],{"categories":4627},[237],{"categories":4629},[210],{"categories":4631},[210],{"categories":4633},[210],{"categories":4635},[210],{"categories":4637},[497],{"categories":4639},[210],{"categories":4641},[223],{"categories":4643},[223],{"categories":4645},[259],{"categories":4647},[210],{"categories":4649},[210],{"categories":4651},[210],{"categories":4653},[223],{"categories":4655},[210],{"categories":4657},[210],{"categories":4659},[210],{"categories":4661},[3244],{"categories":4663},[4664],"Clinical AI",{"categories":4666},[310],{"categories":4668},[210],{"categories":4670},[210],{"categories":4672},[210],{"categories":4674},[210],{"categories":4676},[380],{"categories":4678},[2603],{"categories":4680},[210],{"categories":4682},[226],{"categories":4684},[310],{"categories":4686},[210],{"categories":4688},[223],{"categories":4690},[210],{"categories":4692},[210],{"categories":4694},[259],{"categories":4696},[210],{"categories":4698},[223],{"categories":4700},[237],{"categories":4702},[339],{"categories":4704},[210],{"categories":4706},[210],{"categories":4708},[218],{"categories":4710},[210],{"categories":4712},[210],{"categories":4714},[614],{"categories":4716},[210],{"categories":4718},[],{"categories":4720},[223],{"categories":4722},[210],{"categories":4724},[237],{"categories":4726},[213],{"categories":4728},[210],{"categories":4730},[],{"categories":4732},[],{"categories":4734},[210],{"categories":4736},[],{"categories":4738},[218],{"categories":4740},[210],{"categories":4742},[210],{"categories":4744},[223],{"categories":4746},[210],{"categories":4748},[259],{"categories":4750},[259],{"categories":4752},[259],{"categories":4754},[259],{"categories":4756},[],{"categories":4758},[213],{"categories":4760},[223],{"categories":4762},[259],{"categories":4764},[210],{"categories":4766},[687],{"categories":4768},[226],{"categories":4770},[223],{"categories":4772},[210],{"categories":4774},[213],{"categories":4776},[210],{"categories":4778},[223],{"categories":4780},[210],{"categories":4782},[210],{"categories":4784},[210],{"categories":4786},[210,223],{"categories":4788},[223],{"categories":4790},[380],{"categories":4792},[259],{"categories":4794},[223],{"categories":4796},[259],{"categories":4798},[223],{"categories":4800},[210],{"categories":4802},[],{"categories":4804},[259],{"categories":4806},[339],{"categories":4808},[213],{"categories":4810},[210],{"categories":4812},[210],{"categories":4814},[],{"categories":4816},[237],{"categories":4818},[],{"categories":4820},[213],{"categories":4822},[223],{"categories":4824},[259],{"categories":4826},[210],{"categories":4828},[259],{"categories":4830},[213],{"categories":4832},[259],{"categories":4834},[259],{"categories":4836},[],{"categories":4838},[218],{"categories":4840},[223],{"categories":4842},[259],{"categories":4844},[259],{"categories":4846},[259],{"categories":4848},[259],{"categories":4850},[259],{"categories":4852},[259],{"categories":4854},[259],{"categories":4856},[259],{"categories":4858},[259],{"categories":4860},[259],{"categories":4862},[185],{"categories":4864},[213],{"categories":4866},[210],{"categories":4868},[210],{"categories":4870},[223],{"categories":4872},[223],{"categories":4874},[],{"categories":4876},[210],{"categories":4878},[210,213],{"categories":4880},[],{"categories":4882},[223],{"categories":4884},[210],{"categories":4886},[259],{"categories":4888},[223],{"categories":4890},[1012],{"categories":4892},[210],{"categories":4894},[210],{"categories":4896},[210],{"categories":4898},[210],{"categories":4900},[210],{"categories":4902},[474],{"categories":4904},[210],{"categories":4906},[210],{"categories":4908},[223],{"categories":4910},[210],{"categories":4912},[210],{"categories":4914},[218],{"categories":4916},[226],{"categories":4918},[223],{"categories":4920},[223],{"categories":4922},[],{"categories":4924},[223],{"categories":4926},[310],{"categories":4928},[259],{"categories":4930},[210],{"categories":4932},[],{"categories":4934},[226],{"categories":4936},[],{"categories":4938},[237],{"categories":4940},[210],{"categories":4942},[223],{"categories":4944},[310],{"categories":4946},[210],{"categories":4948},[],{"categories":4950},[210],{"categories":4952},[210],{"categories":4954},[],{"categories":4956},[339],{"categories":4958},[210],{"categories":4960},[223],{"categories":4962},[],{"categories":4964},[],{"categories":4966},[259],{"categories":4968},[213],{"categories":4970},[210],{"categories":4972},[210],{"categories":4974},[218],{"categories":4976},[210],{"categories":4978},[210],{"categories":4980},[223],{"categories":4982},[210],{"categories":4984},[218],{"categories":4986},[218],{"categories":4988},[310],{"categories":4990},[],{"categories":4992},[210],{"categories":4994},[259],{"categories":4996},[],{"categories":4998},[210],{"categories":5000},[210],{"categories":5002},[310],{"categories":5004},[210],{"categories":5006},[210],{"categories":5008},[339],{"categories":5010},[210],{"categories":5012},[380],{"categories":5014},[],{"categories":5016},[223],{"categories":5018},[210],{"categories":5020},[339],{"categories":5022},[237],{"categories":5024},[],{"categories":5026},[210],{"categories":5028},[],{"categories":5030},[223],{"categories":5032},[310],{"categories":5034},[237],{"categories":5036},[],{"categories":5038},[3244],{"categories":5040},[218],{"categories":5042},[213],{"categories":5044},[210],{"categories":5046},[185],{"categories":5048},[223],{"categories":5050},[310],{"categories":5052},[210],{"categories":5054},[237],{"categories":5056},[],{"categories":5058},[],{"categories":5060},[210],{"categories":5062},[213],{"categories":5064},[210],{"categories":5066},[339],{"categories":5068},[],{"categories":5070},[223],{"categories":5072},[223],{"categories":5074},[210],{"categories":5076},[223],{"categories":5078},[210],{"categories":5080},[259],{"categories":5082},[237],{"categories":5084},[210],{"categories":5086},[223],{"categories":5088},[226],{"categories":5090},[210],{"categories":5092},[210],{"categories":5094},[210],{"categories":5096},[223],{"categories":5098},[210],{"categories":5100},[226],{"categories":5102},[339],{"categories":5104},[259],{"categories":5106},[],{"categories":5108},[339],{"categories":5110},[210],{"categories":5112},[],{"categories":5114},[237],{"categories":5116},[223],{"categories":5118},[],{"categories":5120},[210],{"categories":5122},[210],{"categories":5124},[210],{"categories":5126},[210],{"categories":5128},[210],{"categories":5130},[223],{"categories":5132},[218],{"categories":5134},[213],{"categories":5136},[223],{"categories":5138},[210],{"categories":5140},[310],{"categories":5142},[237],{"categories":5144},[237],{"categories":5146},[210],{"categories":5148},[185],{"categories":5150},[223],{"categories":5152},[210],{"categories":5154},[210],{"categories":5156},[223],{"categories":5158},[210],{"categories":5160},[210],{"categories":5162},[223],{"categories":5164},[218],{"categories":5166},[210],{"categories":5168},[310],{"categories":5170},[237],{"categories":5172},[223],{"categories":5174},[210],{"categories":5176},[226],{"categories":5178},[210],{"categories":5180},[223],{"categories":5182},[210],{"categories":5184},[210],{"categories":5186},[259],{"categories":5188},[210],{"categories":5190},[],{"categories":5192},[213],{"categories":5194},[210],{"categories":5196},[210],{"categories":5198},[210],{"categories":5200},[237],{"categories":5202},[237],{"categories":5204},[210],{"categories":5206},[237],{"categories":5208},[210],{"categories":5210},[223],{"categories":5212},[210],{"categories":5214},[210],{"categories":5216},[210],{"categories":5218},[210],{"categories":5220},[210],{"categories":5222},[],{"categories":5224},[210],{"categories":5226},[310],{"categories":5228},[223],{"categories":5230},[218],{"categories":5232},[259],{"categories":5234},[210],{"categories":5236},[223],{"categories":5238},[210],{"categories":5240},[223],{"categories":5242},[210],{"categories":5244},[210],{"categories":5246},[310],{"categories":5248},[223],{"categories":5250},[210],{"categories":5252},[339],{"categories":5254},[210],{"categories":5256},[185],{"categories":5258},[210],{"categories":5260},[210],{"categories":5262},[259],{"categories":5264},[210],{"categories":5266},[210],{"categories":5268},[210],{"categories":5270},[210],{"categories":5272},[223],{"categories":5274},[380],{"categories":5276},[210],{"categories":5278},[237],{"categories":5280},[223],{"categories":5282},[185],{"categories":5284},[],{"categories":5286},[223],{"categories":5288},[237],{"categories":5290},[210],{"categories":5292},[210],{"categories":5294},[2439],{"categories":5296},[310],{"categories":5298},[409],{"categories":5300},[210],{"categories":5302},[210],{"categories":5304},[210],{"categories":5306},[210],{"categories":5308},[213],{"categories":5310},[210],{"categories":5312},[210],{"categories":5314},[237],{"categories":5316},[218],{"categories":5318},[210],{"categories":5320},[237],{"categories":5322},[210],{"categories":5324},[],{"categories":5326},[223],{"categories":5328},[223],{"categories":5330},[210],{"categories":5332},[210],{"categories":5334},[210],{"categories":5336},[185],{"categories":5338},[],{"categories":5340},[259],{"categories":5342},[],{"categories":5344},[259],{"categories":5346},[210],{"categories":5348},[210],{"categories":5350},[223],{"categories":5352},[210],{"categories":5354},[223],{"categories":5356},[223],{"categories":5358},[],{"categories":5360},[210],{"categories":5362},[259],{"categories":5364},[210],{"categories":5366},[],{"categories":5368},[210],{"categories":5370},[210],{"categories":5372},[],{"categories":5374},[210],{"categories":5376},[210],{"categories":5378},[310],{"categories":5380},[237],{"categories":5382},[223],{"categories":5384},[210],{"categories":5386},[210],{"categories":5388},[210],{"categories":5390},[210],{"categories":5392},[339],{"categories":5394},[210],{"categories":5396},[210],{"categories":5398},[210],{"categories":5400},[213],{"categories":5402},[210],{"categories":5404},[210],{"categories":5406},[],{"categories":5408},[210],{"categories":5410},[210],{"categories":5412},[210],{"categories":5414},[],{"categories":5416},[213],{"categories":5418},[210],{"categories":5420},[210],{"categories":5422},[259],{"categories":5424},[237],{"categories":5426},[226],{"categories":5428},[223],{"categories":5430},[553],{"categories":5432},[210],{"categories":5434},[210],{"categories":5436},[210],{"categories":5438},[237],{"categories":5440},[259],{"categories":5442},[310],{"categories":5444},[210],{"categories":5446},[210],{"categories":5448},[210],{"categories":5450},[210],{"categories":5452},[259],{"categories":5454},[210],{"categories":5456},[310],{"categories":5458},[210],{"categories":5460},[210],{"categories":5462},[259],{"categories":5464},[310],{"categories":5466},[210],{"categories":5468},[259],{"categories":5470},[210],{"categories":5472},[223],{"categories":5474},[223],{"categories":5476},[223],{"categories":5478},[237],{"categories":5480},[259],{"categories":5482},[223],{"categories":5484},[223],{"categories":5486},[210],{"categories":5488},[237],{"categories":5490},[310],{"categories":5492},[210],{"categories":5494},[210],{"categories":5496},[223],{"categories":5498},[210],{"categories":5500},[],{"categories":5502},[223],{"categories":5504},[],{"categories":5506},[210],{"categories":5508},[210],{"categories":5510},[],{"categories":5512},[],{"categories":5514},[223],{"categories":5516},[218],{"categories":5518},[223],{"categories":5520},[5521],"Liability & Ethics",{"categories":5523},[210],{"categories":5525},[210],{"categories":5527},[210],{"categories":5529},[223],{"categories":5531},[213],{"categories":5533},[223],{"categories":5535},[218],{"categories":5537},[339],{"categories":5539},[223],{"categories":5541},[210],{"categories":5543},[210],{"categories":5545},[],{"categories":5547},[660],{"categories":5549},[223],{"categories":5551},[],{"categories":5553},[210],{"categories":5555},[213],{"categories":5557},[223],{"categories":5559},[],{"categories":5561},[223],{"categories":5563},[210],{"categories":5565},[210],{"categories":5567},[237],{"categories":5569},[210],{"categories":5571},[259],{"categories":5573},[210],{"categories":5575},[210],{"categories":5577},[226],{"categories":5579},[223],{"categories":5581},[210],{"categories":5583},[210],{"categories":5585},[210],{"categories":5587},[259],{"categories":5589},[223],{"categories":5591},[237],{"categories":5593},[310],{"categories":5595},[213],{"categories":5597},[210],{"categories":5599},[210],{"categories":5601},[210],{"categories":5603},[],{"categories":5605},[223],{"categories":5607},[223],{"categories":5609},[223],{"categories":5611},[553],{"categories":5613},[310],{"categories":5615},[223],{"categories":5617},[380],{"categories":5619},[237],{"categories":5621},[259],{"categories":5623},[210],{"categories":5625},[310],{"categories":5627},[210],{"categories":5629},[213],{"categories":5631},[],{"categories":5633},[223],{"categories":5635},[210],{"categories":5637},[210],{"categories":5639},[210],{"categories":5641},[210],{"categories":5643},[223],{"categories":5645},[210],{"categories":5647},[210],{"categories":5649},[310],{"categories":5651},[],{"categories":5653},[223],{"categories":5655},[226],{"categories":5657},[259],{"categories":5659},[223],{"categories":5661},[218],{"categories":5663},[],{"categories":5665},[210],{"categories":5667},[210],{"categories":5669},[226],{"categories":5671},[210],{"categories":5673},[223],{"categories":5675},[259],{"categories":5677},[213],{"categories":5679},[380],{"categories":5681},[210],{"categories":5683},[210],{"categories":5685},[210],{"categories":5687},[259],{"categories":5689},[218],{"categories":5691},[210],{"categories":5693},[310],{"categories":5695},[259],{"categories":5697},[380],{"categories":5699},[210],{"categories":5701},[223],{"categories":5703},[],{"categories":5705},[614],{"categories":5707},[],{"categories":5709},[210],{"categories":5711},[380],{"categories":5713},[210],{"categories":5715},[185],{"categories":5717},[210],{"categories":5719},[223],{"categories":5721},[223],{"categories":5723},[5724],"Design News & Tools",{"categories":5726},[210],{"categories":5728},[210],{"categories":5730},[259],{"categories":5732},[210],{"categories":5734},[210],{"categories":5736},[213],{"categories":5738},[223],{"categories":5740},[210],{"categories":5742},[310],{"categories":5744},[223],{"categories":5746},[223],{"categories":5748},[310],{"categories":5750},[210],{"categories":5752},[210],{"categories":5754},[553],{"categories":5756},[223],{"categories":5758},[210],{"categories":5760},[210],{"categories":5762},[553],{"categories":5764},[210],{"categories":5766},[339],{"categories":5768},[210],{"categories":5770},[223],{"categories":5772},[],{"categories":5774},[210],{"categories":5776},[210],{"categories":5778},[210],{"categories":5780},[259],{"categories":5782},[210],{"categories":5784},[213],{"categories":5786},[],{"categories":5788},[210],{"categories":5790},[210],{"categories":5792},[210],{"categories":5794},[237],{"categories":5796},[687],{"categories":5798},[237],{"categories":5800},[310],{"categories":5802},[210],{"categories":5804},[210,223],{"categories":5806},[339,218],{"categories":5808},[237],{"categories":5810},[210],{"categories":5812},[210],{"categories":5814},[210],{"categories":5816},[210],{"categories":5818},[],{"categories":5820},[223],{"categories":5822},[210],{"categories":5824},[],{"categories":5826},[210],{"categories":5828},[237],{"categories":5830},[210],{"categories":5832},[237],{"categories":5834},[],{"categories":5836},[223],{"categories":5838},[210],{"categories":5840},[218],{"categories":5842},[210],{"categories":5844},[259],{"categories":5846},[210],{"categories":5848},[],{"categories":5850},[223],{"categories":5852},[210],{"categories":5854},[],{"categories":5856},[310],{"categories":5858},[210],{"categories":5860},[210],{"categories":5862},[223],{"categories":5864},[210],{"categories":5866},[210],{"categories":5868},[213],{"categories":5870},[223],{"categories":5872},[210],{"categories":5874},[],{"categories":5876},[210],{"categories":5878},[380],{"categories":5880},[339],{"categories":5882},[218],{"categories":5884},[218],{"categories":5886},[210],{"categories":5888},[213],{"categories":5890},[213],{"categories":5892},[210],{"categories":5894},[223],{"categories":5896},[210],{"categories":5898},[210],{"categories":5900},[210],{"categories":5902},[210],{"categories":5904},[237],{"categories":5906},[210],{"categories":5908},[213],{"categories":5910},[210],{"categories":5912},[210],{"categories":5914},[223],{"categories":5916},[210],{"categories":5918},[339],{"categories":5920},[210],{"categories":5922},[259],{"categories":5924},[210],{"categories":5926},[210],{"categories":5928},[223],{"categories":5930},[226],{"categories":5932},[210],{"categories":5934},[210],{"categories":5936},[223],{"categories":5938},[],{"categories":5940},[237],{"categories":5942},[],{"categories":5944},[237],{"categories":5946},[223],{"categories":5948},[213],{"categories":5950},[210],{"categories":5952},[],{"categories":5954},[185],{"categories":5956},[380],{"categories":5958},[210],{"categories":5960},[237],{"categories":5962},[210],{"categories":5964},[],{"categories":5966},[259],{"categories":5968},[223],{"categories":5970},[237],{"categories":5972},[310],{"categories":5974},[218],{"categories":5976},[210],{"categories":5978},[210],{"categories":5980},[223],{"categories":5982},[237],{"categories":5984},[223],{"categories":5986},[259],{"categories":5988},[210],{"categories":5990},[226],{"categories":5992},[213],{"categories":5994},[226],{"categories":5996},[259],{"categories":5998},[210],{"categories":6000},[237],{"categories":6002},[210],{"categories":6004},[310],{"categories":6006},[218],{"categories":6008},[210],{"categories":6010},[210],{"categories":6012},[210],{"categories":6014},[210],{"categories":6016},[210],{"categories":6018},[210],{"categories":6020},[223],{"categories":6022},[210],{"categories":6024},[223],{"categories":6026},[210],{"categories":6028},[210],{"categories":6030},[213],{"categories":6032},[210],{"categories":6034},[223],{"categories":6036},[223],{"categories":6038},[310],{"categories":6040},[223],{"categories":6042},[223],{"categories":6044},[210],{"categories":6046},[213],{"categories":6048},[223],{"categories":6050},[310],{"categories":6052},[],{"categories":6054},[210],{"categories":6056},[185],{"categories":6058},[553],{"categories":6060},[210],{"categories":6062},[223],{"categories":6064},[210],{"categories":6066},[210],{"categories":6068},[237],{"categories":6070},[210],{"categories":6072},[],{"categories":6074},[210],{"categories":6076},[223],{"categories":6078},[210],{"categories":6080},[339],{"categories":6082},[210],{"categories":6084},[237],{"categories":6086},[210],{"categories":6088},[259],{"categories":6090},[223],{"categories":6092},[210],{"categories":6094},[339],{"categories":6096},[223],{"categories":6098},[218],{"categories":6100},[218],{"categories":6102},[210],{"categories":6104},[210],{"categories":6106},[210],{"categories":6108},[210],{"categories":6110},[210],{"categories":6112},[210],{"categories":6114},[213],{"categories":6116},[],{"categories":6118},[210],{"categories":6120},[210],{"categories":6122},[223],{"categories":6124},[210],{"categories":6126},[223],{"categories":6128},[210],{"categories":6130},[210],{"categories":6132},[210],{"categories":6134},[210],{"categories":6136},[210],{"categories":6138},[237],{"categories":6140},[],{"categories":6142},[213],{"categories":6144},[210],{"categories":6146},[210],{"categories":6148},[223],{"categories":6150},[223],{"categories":6152},[],{"categories":6154},[237],{"categories":6156},[237],{"categories":6158},[210],{"categories":6160},[339],{"categories":6162},[218],{"categories":6164},[310],{"categories":6166},[],{"categories":6168},[210],{"categories":6170},[223],{"categories":6172},[213],{"categories":6174},[210],{"categories":6176},[210],{"categories":6178},[237],{"categories":6180},[213],{"categories":6182},[210],{"categories":6184},[210],{"categories":6186},[259],{"categories":6188},[185],{"categories":6190},[210],{"categories":6192},[259],{"categories":6194},[223],{"categories":6196},[210],{"categories":6198},[],{"categories":6200},[259],{"categories":6202},[223],{"categories":6204},[310],{"categories":6206},[185],{"categories":6208},[210],{"categories":6210},[210],{"categories":6212},[],{"categories":6214},[223],{"categories":6216},[223],{"categories":6218},[223],{"categories":6220},[3244],{"categories":6222},[259],{"categories":6224},[210],{"categories":6226},[237],{"categories":6228},[210],{"categories":6230},[210],{"categories":6232},[210],{"categories":6234},[210],{"categories":6236},[210],{"categories":6238},[218],{"categories":6240},[210],{"categories":6242},[213],{"categories":6244},[1901],{"categories":6246},[380],{"categories":6248},[213],{"categories":6250},[],{"categories":6252},[210],{"categories":6254},[],{"categories":6256},[259],{"categories":6258},[223],{"categories":6260},[310],{"categories":6262},[210],{"categories":6264},[210],{"categories":6266},[210],{"categories":6268},[259],{"categories":6270},[],{"categories":6272},[223],{"categories":6274},[210],{"categories":6276},[223],{"categories":6278},[223],{"categories":6280},[],{"categories":6282},[210],{"categories":6284},[],{"categories":6286},[259],{"categories":6288},[213],{"categories":6290},[310],{"categories":6292},[210],{"categories":6294},[223],{"categories":6296},[259],{"categories":6298},[210],{"categories":6300},[259],{"categories":6302},[],{"categories":6304},[259],{"categories":6306},[210],{"categories":6308},[213],{"categories":6310},[553],{"categories":6312},[223],{"categories":6314},[210],{"categories":6316},[],{"categories":6318},[237],{"categories":6320},[223],{"categories":6322},[226],{"categories":6324},[223],{"categories":6326},[213],{"categories":6328},[210],{"categories":6330},[210],{"categories":6332},[],{"categories":6334},[],{"categories":6336},[],{"categories":6338},[310],{"categories":6340},[210],{"categories":6342},[223],{"categories":6344},[210],{"categories":6346},[210],{"categories":6348},[],{"categories":6350},[],{"categories":6352},[],{"categories":6354},[210],{"categories":6356},[223],{"categories":6358},[310],{"categories":6360},[210],{"categories":6362},[],{"categories":6364},[223],{"categories":6366},[210],{"categories":6368},[210],{"categories":6370},[213],{"categories":6372},[],{"categories":6374},[],{"categories":6376},[210],{"categories":6378},[210],{"categories":6380},[223],{"categories":6382},[310],{"categories":6384},[210],{"categories":6386},[259],{"categories":6388},[],{"categories":6390},[210],{"categories":6392},[210],{"categories":6394},[339],{"categories":6396},[259],{"categories":6398},[339],{"categories":6400},[185],{"categories":6402},[210],{"categories":6404},[210],{"categories":6406},[],{"categories":6408},[],{"categories":6410},[223],{"categories":6412},[],{"categories":6414},[210],{"categories":6416},[553],{"categories":6418},[210],{"categories":6420},[210],{"categories":6422},[210],{"categories":6424},[210],{"categories":6426},[],{"categories":6428},[223],{"categories":6430},[210],{"categories":6432},[210],{"categories":6434},[],{"categories":6436},[223],{"categories":6438},[210],{"categories":6440},[259],{"categories":6442},[210],{"categories":6444},[339],{"categories":6446},[218],{"categories":6448},[226],{"categories":6450},[210],{"categories":6452},[210],{"categories":6454},[223],{"categories":6456},[185],{"categories":6458},[223],{"categories":6460},[223],{"categories":6462},[],{"categories":6464},[210],{"categories":6466},[223],{"categories":6468},[],{"categories":6470},[210],{"categories":6472},[],{"categories":6474},[259],{"categories":6476},[218],{"categories":6478},[],{"categories":6480},[210],{"categories":6482},[210],{"categories":6484},[210],{"categories":6486},[],{"categories":6488},[223],{"categories":6490},[310],{"categories":6492},[213],{"categories":6494},[210],{"categories":6496},[],{"categories":6498},[218],{"categories":6500},[339],{"categories":6502},[210],{"categories":6504},[237],{"categories":6506},[213],{"categories":6508},[185],{"categories":6510},[218],{"categories":6512},[237],{"categories":6514},[223],{"categories":6516},[237],{"categories":6518},[],{"categories":6520},[210],{"categories":6522},[226],{"categories":6524},[210],{"categories":6526},[],{"categories":6528},[223],{"categories":6530},[213],{"categories":6532},[310],{"categories":6534},[210],{"categories":6536},[213],{"categories":6538},[223],{"categories":6540},[380],{"categories":6542},[210],{"categories":6544},[210],{"categories":6546},[210],{"categories":6548},[210],{"categories":6550},[210],{"categories":6552},[213],{"categories":6554},[210],{"categories":6556},[237],{"categories":6558},[185],{"categories":6560},[223],{"categories":6562},[],{"categories":6564},[210],{"categories":6566},[210],{"categories":6568},[210],{"categories":6570},[237],{"categories":6572},[223],{"categories":6574},[259],{"categories":6576},[237],{"categories":6578},[210],{"categories":6580},[226],{"categories":6582},[],{"categories":6584},[310],{"categories":6586},[237],{"categories":6588},[259],{"categories":6590},[210],{"categories":6592},[213],{"categories":6594},[223],{"categories":6596},[210],{"categories":6598},[210],{"categories":6600},[223],{"categories":6602},[226],{"categories":6604},[210],{"categories":6606},[223],{"categories":6608},[210],{"categories":6610},[218],{"categories":6612},[223],{"categories":6614},[223,380],{"categories":6616},[210],{"categories":6618},[210],{"categories":6620},[223],{"categories":6622},[237],{"categories":6624},[210],{"categories":6626},[210],{"categories":6628},[185],{"categories":6630},[223],{"categories":6632},[339],{"categories":6634},[223],{"categories":6636},[218],{"categories":6638},[],{"categories":6640},[223],{"categories":6642},[210],{"categories":6644},[218],{"categories":6646},[],{"categories":6648},[],{"categories":6650},[237],{"categories":6652},[210],{"categories":6654},[210],{"categories":6656},[223],{"categories":6658},[185],{"categories":6660},[339],{"categories":6662},[210],{"categories":6664},[210],{"categories":6666},[210],{"categories":6668},[223],{"categories":6670},[],{"categories":6672},[223],{"categories":6674},[259],{"categories":6676},[210],{"categories":6678},[223],{"categories":6680},[223],{"categories":6682},[210],{"categories":6684},[],{"categories":6686},[259],{"categories":6688},[237],{"categories":6690},[3244],{"categories":6692},[213],{"categories":6694},[237],{"categories":6696},[210],{"categories":6698},[223],{"categories":6700},[210],{"categories":6702},[210],{"categories":6704},[339],{"categories":6706},[237],{"categories":6708},[185],{"categories":6710},[],{"categories":6712},[259],{"categories":6714},[210],{"categories":6716},[210],{"categories":6718},[],{"categories":6720},[223],{"categories":6722},[210],{"categories":6724},[210],{"categories":6726},[210],{"categories":6728},[210],{"categories":6730},[223],{"categories":6732},[210],{"categories":6734},[210],{"categories":6736},[210],{"categories":6738},[226],{"categories":6740},[210],{"categories":6742},[223],{"categories":6744},[210],{"categories":6746},[210],{"categories":6748},[210],{"categories":6750},[210],{"categories":6752},[210],{"categories":6754},[210],{"categories":6756},[210],{"categories":6758},[218],{"categories":6760},[],{"categories":6762},[226],{"categories":6764},[259],{"categories":6766},[223],{"categories":6768},[210],{"categories":6770},[237],{"categories":6772},[],{"categories":6774},[237],{"categories":6776},[237],{"categories":6778},[223],{"categories":6780},[237],{"categories":6782},[210],{"categories":6784},[210],{"categories":6786},[210],{"categories":6788},[223],{"categories":6790},[237],{"categories":6792},[210],{"categories":6794},[210],{"categories":6796},[210],{"categories":6798},[223],{"categories":6800},[259],{"categories":6802},[210],{"categories":6804},[210],{"categories":6806},[210],{"categories":6808},[218],{"categories":6810},[210],{"categories":6812},[223],{"categories":6814},[310],{"categories":6816},[],{"categories":6818},[210],{"categories":6820},[185],{"categories":6822},[210],{"categories":6824},[223],{"categories":6826},[210],{"categories":6828},[210],{"categories":6830},[],{"categories":6832},[210],{"categories":6834},[210],{"categories":6836},[259],{"categories":6838},[210],{"categories":6840},[210],{"categories":6842},[223],{"categories":6844},[339],{"categories":6846},[],{"categories":6848},[],{"categories":6850},[237],{"categories":6852},[210],{"categories":6854},[210],{"categories":6856},[259],{"categories":6858},[210],{"categories":6860},[237],{"categories":6862},[259],{"categories":6864},[210],{"categories":6866},[210],{"categories":6868},[339],{"categories":6870},[185],{"categories":6872},[210],{"categories":6874},[210],{"categories":6876},[213],{"categories":6878},[223],{"categories":6880},[210],{"categories":6882},[210],{"categories":6884},[223],{"categories":6886},[218],{"categories":6888},[223],{"categories":6890},[237],{"categories":6892},[210],{"categories":6894},[218],{"categories":6896},[],{"categories":6898},[210],{"categories":6900},[185],{"categories":6902},[210],{"categories":6904},[210],{"categories":6906},[],{"categories":6908},[259],{"categories":6910},[210],{"categories":6912},[223],{"categories":6914},[185],{"categories":6916},[210],{"categories":6918},[237],{"categories":6920},[237],{"categories":6922},[237],{"categories":6924},[210],{"categories":6926},[223],{"categories":6928},[223],{"categories":6930},[210],{"categories":6932},[223],{"categories":6934},[210],{"categories":6936},[210],{"categories":6938},[310],{"categories":6940},[185],{"categories":6942},[185],{"categories":6944},[],{"categories":6946},[259],{"categories":6948},[210],{"categories":6950},[210],{"categories":6952},[237],{"categories":6954},[],{"categories":6956},[259],{"categories":6958},[259],{"categories":6960},[259],{"categories":6962},[],{"categories":6964},[223],{"categories":6966},[210],{"categories":6968},[],{"categories":6970},[213],{"categories":6972},[218],{"categories":6974},[],{"categories":6976},[210],{"categories":6978},[210],{"categories":6980},[],{"categories":6982},[237],{"categories":6984},[],{"categories":6986},[],{"categories":6988},[],{"categories":6990},[],{"categories":6992},[210],{"categories":6994},[259],{"categories":6996},[],{"categories":6998},[],{"categories":7000},[210],{"categories":7002},[210],{"categories":7004},[210],{"categories":7006},[185],{"categories":7008},[210],{"categories":7010},[185],{"categories":7012},[],{"categories":7014},[185],{"categories":7016},[185],{"categories":7018},[380],{"categories":7020},[223],{"categories":7022},[237],{"categories":7024},[],{"categories":7026},[],{"categories":7028},[185],{"categories":7030},[237],{"categories":7032},[237],{"categories":7034},[237],{"categories":7036},[],{"categories":7038},[213],{"categories":7040},[237],{"categories":7042},[237],{"categories":7044},[213],{"categories":7046},[237],{"categories":7048},[218],{"categories":7050},[237],{"categories":7052},[237],{"categories":7054},[237],{"categories":7056},[185],{"categories":7058},[259],{"categories":7060},[259],{"categories":7062},[210],{"categories":7064},[237],{"categories":7066},[185],{"categories":7068},[380],{"categories":7070},[185],{"categories":7072},[185],{"categories":7074},[185],{"categories":7076},[],{"categories":7078},[218],{"categories":7080},[],{"categories":7082},[380],{"categories":7084},[237],{"categories":7086},[237],{"categories":7088},[237],{"categories":7090},[223],{"categories":7092},[259,218],{"categories":7094},[185],{"categories":7096},[],{"categories":7098},[],{"categories":7100},[185],{"categories":7102},[],{"categories":7104},[185],{"categories":7106},[259],{"categories":7108},[223],{"categories":7110},[],{"categories":7112},[237],{"categories":7114},[210],{"categories":7116},[310],{"categories":7118},[],{"categories":7120},[210],{"categories":7122},[],{"categories":7124},[259],{"categories":7126},[213],{"categories":7128},[185],{"categories":7130},[],{"categories":7132},[237],{"categories":7134},[259],[7136,7315,7383,7457],{"id":7137,"title":7138,"ai":7139,"body":7144,"categories":7304,"created_at":186,"date_modified":186,"description":178,"extension":187,"faq":186,"featured":188,"kicker_label":186,"meta":7305,"navigation":190,"path":7306,"published_at":192,"question":186,"scraped_at":186,"seo":7307,"sitemap":7308,"source_id":7309,"source_name":196,"source_type":197,"source_url":198,"stem":7310,"tags":7311,"thumbnail_url":186,"tldr":7312,"tweet":186,"unknown_tags":7313,"__hash__":7314},"summaries\u002Fsummaries\u002Fnumpy-batched-lstm-forward-backward-summary.md","NumPy Batched LSTM Forward\u002FBackward",{"provider":7,"model":8,"input_tokens":7140,"output_tokens":7141,"processing_time_ms":7142,"cost_usd":7143},8684,1415,14034,0.0019739,{"type":14,"value":7145,"toc":7298},[7146,7150,7153,7157,7164,7213,7216,7220,7230,7285,7288,7292,7295],[17,7147,7149],{"id":7148},"parameter-initialization-for-stable-training","Parameter Initialization for Stable Training",[22,7151,7152],{},"LSTM weights form a single matrix WLSTM of shape (input_size + hidden_size + 1, 4 * hidden_size), with +1 for biases as the first row. Use Xavier initialization: random normal scaled by 1\u002Fsqrt(input_size + hidden_size). Set biases to zero initially, but apply 'fancy_forget_bias_init=3' to forget gate biases (indices hidden_size:2*hidden_size) to start with negative bias, encouraging forget gates to stay off early in training since raw gate outputs are ~N(0,1).",[17,7154,7156],{"id":7155},"batched-forward-pass-logic","Batched Forward Pass Logic",[22,7158,7159,7160,7163],{},"Input X: (n,b,input_size). Hidden d = WLSTM.shape",[77,7161,7162],{},"1","\u002F4. Init c0\u002Fh0 as zeros((b,d)) if None. For each timestep t:",[7165,7166,7167,7187,7196,7199,7205],"ul",{},[7168,7169,7170,7171,7174,7175,7178,7179,7182,7183,7186],"li",{},"Build Hin",[77,7172,7173],{},"t,:,0","=1 (bias), Hin",[77,7176,7177],{},"t,:,1:input_size+1","=X",[77,7180,7181],{},"t",", Hin",[77,7184,7185],{},"t,:,input_size+1:","=prev_h (h0 at t=0).",[7168,7188,7189,7190,7192,7193,7195],{},"Compute raw IFOG",[77,7191,7181],{}," = Hin",[77,7194,7181],{}," @ WLSTM (main compute).",[7168,7197,7198],{},"Gates: sigmoid on first 3*d (input\u002Fforget\u002Foutput), tanh on last d (gate candidate).",[7168,7200,7201,7202,7204],{},"Cell C",[77,7203,7181],{}," = input_gate * gate_candidate + forget_gate * prev_c.",[7168,7206,7207,7208,7210,7211,80],{},"Output Hout",[77,7209,7181],{}," = output_gate * tanh(C",[77,7212,7181],{},[22,7214,7215],{},"Cache stores all intermediates (Hin, IFOG, IFOGf, C, Ct, etc.) for backward. Returns full Hout (n,b,d), final C\u002FH, cache.",[17,7217,7219],{"id":7218},"backward-pass-gradient-computation","Backward Pass Gradient Computation",[22,7221,7222,7223,7226,7227,7229],{},"Input dHout_in (n,b,d). Accumulate dC",[77,7224,7225],{},"n-1","\u002FdHout",[77,7228,7225],{}," if provided for state carryover. Reverse loop over t:",[7165,7231,7232,7240,7249,7252,7267],{},[7168,7233,7234,7235,7237,7238,103],{},"dIFOGf output slice (2d:3d) = tanh(Ct",[77,7236,7181],{},") * dHout",[77,7239,7181],{},[7168,7241,7242,7243,7245,7246,7248],{},"dC",[77,7244,7181],{}," from tanh' * output_gate * dHout",[77,7247,7181],{},", plus forget\u002Finput contributions to prev_c.",[7168,7250,7251],{},"Backprop activations: tanh' on gate candidate, sigmoid'=(y(1-y)) on gates.",[7168,7253,7254,7255,7257,7258,7260,7261,7263,7264,7266],{},"dWLSTM += Hin",[77,7256,7181],{},".T @ dIFOG",[77,7259,7181],{},"; dHin",[77,7262,7181],{}," = dIFOG",[77,7265,7181],{}," @ WLSTM.T.",[7168,7268,7269,7270,7272,7273,7276,7277,7280,7281,7284],{},"Extract dX",[77,7271,7181],{}," = dHin",[77,7274,7275],{},"t,1:input+1","; propagate dHout",[77,7278,7279],{},"t-1","\u002Fdh0 from dHin",[77,7282,7283],{},"t,input+1:","; dc0\u002Fdh0 similarly.",[22,7286,7287],{},"Returns dX (n,b,input), dWLSTM, dc0, dh0.",[17,7289,7291],{"id":7290},"verification-ensures-correctness","Verification Ensures Correctness",[22,7293,7294],{},"Test 1 (sequential vs batch): n=5,b=3,d=4,input=10. Run forward sequentially (one timestep at a time, carrying c\u002Fh), confirm Hout matches full batch forward.",[22,7296,7297],{},"Test 2 (gradient check): Numerical grad = (fwd(+δ) - fwd(-δ))\u002F(2δ), δ=1e-5. Relative error threshold warning=1e-2, error=1. Checks every element of X\u002FWLSTM\u002Fc0\u002Fh0 against analytic grads from loss = sum(H * wrand). All params pass with low error, confirming backprop accuracy.",{"title":178,"searchDepth":179,"depth":179,"links":7299},[7300,7301,7302,7303],{"id":7148,"depth":179,"text":7149},{"id":7155,"depth":179,"text":7156},{"id":7218,"depth":179,"text":7219},{"id":7290,"depth":179,"text":7291},[185],{},"\u002Fsummaries\u002Fnumpy-batched-lstm-forward-backward-summary",{"title":7138,"description":178},{"loc":7306},"ed69ec8dcc565dc4","summaries\u002Fnumpy-batched-lstm-forward-backward-summary",[201,202,203],"Efficient pure NumPy LSTM processes batched sequences (n,b,input_size); init with Xavier + forget bias=3; verified via sequential match and numerical gradients.",[],"5dD3n1TS6LbPVttHG7t_U-CvXEtPl5LjBkztvfD9Gxw",{"id":7316,"title":7317,"ai":7318,"body":7323,"categories":7372,"created_at":186,"date_modified":186,"description":178,"extension":187,"faq":186,"featured":188,"kicker_label":186,"meta":7373,"navigation":190,"path":7374,"published_at":192,"question":186,"scraped_at":186,"seo":7375,"sitemap":7376,"source_id":7377,"source_name":196,"source_type":197,"source_url":198,"stem":7378,"tags":7379,"thumbnail_url":186,"tldr":7380,"tweet":186,"unknown_tags":7381,"__hash__":7382},"summaries\u002Fsummaries\u002Fpolicy-gradients-for-pong-100-line-rl-agent-summary.md","Policy Gradients for Pong: 100-Line RL Agent",{"provider":7,"model":8,"input_tokens":7319,"output_tokens":7320,"processing_time_ms":7321,"cost_usd":7322},12952,1480,13868,0.00286,{"type":14,"value":7324,"toc":7366},[7325,7329,7332,7335,7339,7346,7350,7356,7360,7363],[17,7326,7328],{"id":7327},"network-architecture-and-forwardbackward-passes","Network Architecture and Forward\u002FBackward Passes",[22,7330,7331],{},"Build a fully connected policy network with 200 ReLU hidden units: input is 80x80=6400D (binary diff frame), W1 (200x6400 Xavier init), ReLU, W2 (200x1), sigmoid for P(UP=action 2). Forward: h = ReLU(W1 @ x), p = sigmoid(W2 @ h). Sample action stochastically: UP if uniform() \u003C p else DOWN.",[22,7333,7334],{},"Backward computes policy gradient analytically. For episode: stack epx (inputs), eph (hiddens), epdlogp (y - p where y=1 for UP). dW2 = eph.T @ epdlogp. dh = epdlogp.outer(W2), zero ReLU grads (eph\u003C=0), dW1 = dh.T @ epx. Accumulate into grad_buffer over batch_size=10 episodes.",[17,7336,7338],{"id":7337},"image-preprocessing-for-atari-pong","Image Preprocessing for Atari Pong",[22,7340,7341,7342,7345],{},"Transform 210x160x3 uint8 frame: crop top\u002Fbottom to 160x80 (35:195), downsample 2x to 80x80 grayscale (I",[77,7343,7344],{},"::2,::2,0","), binarize (set bg 144\u002F109=0, else=1), flatten to 6400D float. Use difference frames x = cur_x - prev_x (motion highlights ball\u002Fpaddles, zeros static bg). This reduces noise, enables end-to-end from pixels.",[17,7347,7349],{"id":7348},"reward-discounting-and-advantage-normalization","Reward Discounting and Advantage Normalization",[22,7351,7352,7353,7355],{},"Pong rewards: +1 win, -1 lose (sparse, at episode end). For trajectory drs: discount backwards with gamma=0.99, reset running sum at r",[77,7354,7181],{},"!=0 (game boundaries). Standardize discounted_epr to mean=0, std=1 (controls gradient variance). Modulate: epdlogp *= discounted_epr (REINFORCE: grad log pi(a|s) * advantage).",[17,7357,7359],{"id":7358},"training-loop-and-optimization","Training Loop and Optimization",[22,7361,7362],{},"OpenAI Gym Pong-v0. Loop: prepro obs, forward policy, sample\u002Fact, record x\u002Fh\u002Fdlogp\u002Fr. On done: compute discounted\u002Fcentered advantages, backward, add to grad_buffer. Every 10 eps: RMSProp update (decay=0.99, lr=1e-4): g \u002F (sqrt(rms_cache) + 1e-5), reset buffer. Track running_reward (EWMA 0.99), save model every 100 eps. Render optional. Resume from save.p.",[22,7364,7365],{},"Prints episode rewards; agent learns to beat random policy quickly, human-level after ~1-2hr CPU (per blog link in comments).",{"title":178,"searchDepth":179,"depth":179,"links":7367},[7368,7369,7370,7371],{"id":7327,"depth":179,"text":7328},{"id":7337,"depth":179,"text":7338},{"id":7348,"depth":179,"text":7349},{"id":7358,"depth":179,"text":7359},[237],{},"\u002Fsummaries\u002Fpolicy-gradients-for-pong-100-line-rl-agent-summary",{"title":7317,"description":178},{"loc":7374},"7c1c3951efe2f58d","summaries\u002Fpolicy-gradients-for-pong-100-line-rl-agent-summary",[201,202,203],"Train a 2-layer NN to play Atari Pong from raw pixels using REINFORCE policy gradients. Uses 80x80 binary diff frames, discounts rewards with gamma=0.99, standardizes advantages, RMSProp updates every 10 episodes. Converges on CPU in hours.",[],"6XMa-na9tAra5BBDuY7gL83XBVHrWa_0VpHOibbLrNQ",{"id":7384,"title":7385,"ai":7386,"body":7391,"categories":7431,"created_at":186,"date_modified":186,"description":178,"extension":187,"faq":186,"featured":188,"kicker_label":186,"meta":7432,"navigation":190,"path":7443,"published_at":7444,"question":186,"scraped_at":7445,"seo":7446,"sitemap":7447,"source_id":7448,"source_name":7449,"source_type":197,"source_url":7450,"stem":7451,"tags":7452,"thumbnail_url":186,"tldr":7454,"tweet":186,"unknown_tags":7455,"__hash__":7456},"summaries\u002Fsummaries\u002F03a80d45cc3addfe-preprocessing-swings-cnn-accuracy-from-65-to-87-on-summary.md","Preprocessing Swings CNN Accuracy from 65% to 87% on CIFAR-10",{"provider":7,"model":8,"input_tokens":7387,"output_tokens":7388,"processing_time_ms":7389,"cost_usd":7390},8876,1567,16564,0.00205185,{"type":14,"value":7392,"toc":7426},[7393,7397,7408,7412,7419,7423],[17,7394,7396],{"id":7395},"scale-pixels-to-stabilize-gradients-and-boost-baseline-performance","Scale Pixels to Stabilize Gradients and Boost Baseline Performance",[22,7398,7399,7400,7403,7404,7407],{},"Train CNNs on raw CIFAR-10 images (32x32x3 pixels, 0-255 range) without preprocessing for a 65.47% test accuracy baseline after 10 epochs using Adam optimizer and sparse categorical cross-entropy. Large pixel values (up to 255) cause exploding gradients: ∂L\u002F∂w ≈ 255 × δ, leading to overshooting and oscillations in weight updates. Normalize by dividing by 255.0 to scale to ",[77,7401,7402],{},"0,1",", reducing gradients to 1 × δ for smooth convergence, raising accuracy to 69.38%. Standardization (Z-score: (x - μ)\u002Fσ per channel) matches this at 69.38%, centering data at mean 0 and std 1—E",[77,7405,7406],{},"z"," = 0 and Var(z) = 1 proven via linearity of expectation and variance properties—but offers no extra gain for CNNs on images, as basic normalization suffices for stable training.",[17,7409,7411],{"id":7410},"use-geometric-augmentation-for-invariance-but-avoid-photometric-overkill","Use Geometric Augmentation for Invariance but Avoid Photometric Overkill",[22,7413,7414,7415,7418],{},"Apply geometric augmentations (RandomFlip horizontal, RandomRotation 0.1, RandomZoom 0.1) after normalization, training 20 epochs: accuracy dips to 67.13% on simple CNN, as added variability challenges the model without deeper capacity. These create rotation\u002Fscale\u002Fflip invariance via affine transformations—e.g., flip: x' = -x, rotation: ",[77,7416,7417],{},"cosθ -sinθ; sinθ cosθ",", zoom: s scaling—forcing feature learning (wheels, wings) over memorization. Photometric augmentations (RandomBrightness\u002FContrast 0.2) after normalization catastrophically drop accuracy to 20.62%: clipping saturates pixels to 0\u002F1 (e.g., 0.9 + 0.2 → 1.0), destroying edges\u002Ftextures in low-res 32x32 images, worsening signal-to-noise ratio and erasing discriminative features like airplane wings or cat eyes.",[17,7420,7422],{"id":7421},"stack-normalization-geometric-augs-and-architecture-for-87-accuracy","Stack Normalization, Geometric Augs, and Architecture for 87% Accuracy",[22,7424,7425],{},"Combine Z-score standardization ((X - mean)\u002Fstd, ε=1e-7), geometric augmentations (add RandomTranslation 0.1,0.1), one-hot labels with 0.1 label smoothing (y_smooth = (1-α)y_true + α\u002FK, injecting 0.01 uniform noise across 10 classes to curb overconfidence), and deeper CNN (64-128-256 filters in padded conv blocks, BatchNorm, Dropout 0.2-0.5, MaxPool): achieves 87.32% test accuracy with EarlyStopping (patience=8 on val_acc) and ReduceLROnPlateau (factor=0.5, patience=3). BatchNorm normalizes layer activations: ˆx = (x - μ_B)\u002F√(σ²_B + ε), then γˆx + β for learnable scaling\u002Fshift, stabilizing internal distributions. This pipeline aligns preprocessing with model capacity, proving no single technique wins—success demands tailored combinations avoiding info destruction while enforcing generalization.",{"title":178,"searchDepth":179,"depth":179,"links":7427},[7428,7429,7430],{"id":7395,"depth":179,"text":7396},{"id":7410,"depth":179,"text":7411},{"id":7421,"depth":179,"text":7422},[185],{"content_references":7433,"triage":7438},[7434],{"type":7435,"title":7436,"context":7437},"dataset","CIFAR-10","mentioned",{"relevance":7439,"novelty":7439,"quality":7440,"actionability":7440,"composite":7441,"reasoning":7442},3,4,3.45,"Category: Data Science & Visualization. The article discusses preprocessing techniques that significantly improve CNN accuracy on the CIFAR-10 dataset, which is relevant for AI product builders looking to enhance model performance. It provides actionable insights on normalization and augmentation strategies that can be directly applied in practice.","\u002Fsummaries\u002F03a80d45cc3addfe-preprocessing-swings-cnn-accuracy-from-65-to-87-on-summary","2026-04-20 16:07:06","2026-04-21 15:25:42",{"title":7385,"description":178},{"loc":7443},"03a80d45cc3addfe","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fwhen-preprocessing-helps-and-when-it-hurts-why-your-image-classification-models-accuracy-varies-a6761f20e09e?source=rss----5517fd7b58a6---4","summaries\u002F03a80d45cc3addfe-preprocessing-swings-cnn-accuracy-from-65-to-87-on-summary",[202,203,7453,201],"data-science","Raw CIFAR-10 pixels yield 65% test accuracy; normalization\u002Fstandardization lift to 69%; geometric augmentation maintains ~67%; photometric brightness\u002Fcontrast crashes to 20%; combined pipeline with deeper CNN hits 87%.",[],"w3EW0KbB8oA66HfhVLdBPkpkFdwzACfoXmZTwcAFNMQ",{"id":7458,"title":7459,"ai":7460,"body":7465,"categories":7637,"created_at":186,"date_modified":186,"description":178,"extension":187,"faq":186,"featured":188,"kicker_label":186,"meta":7638,"navigation":190,"path":7647,"published_at":7648,"question":186,"scraped_at":7649,"seo":7650,"sitemap":7651,"source_id":7652,"source_name":7653,"source_type":197,"source_url":7654,"stem":7655,"tags":7656,"thumbnail_url":186,"tldr":7658,"tweet":186,"unknown_tags":7659,"__hash__":7660},"summaries\u002Fsummaries\u002F70fa59cd85bd7438-build-fno-pinn-surrogates-for-darcy-flow-with-phys-summary.md","Build FNO & PINN Surrogates for Darcy Flow with PhysicsNeMo",{"provider":7,"model":8,"input_tokens":7461,"output_tokens":7462,"processing_time_ms":7463,"cost_usd":7464},9889,3106,28970,0.00323995,{"type":14,"value":7466,"toc":7631},[7467,7471,7477,7500,7524,7528,7531,7534,7549,7553,7564,7567,7587,7591,7594,7597,7612,7615,7618,7621,7624,7627],[17,7468,7470],{"id":7469},"synthetic-darcy-flow-data-pipeline-from-grf-permeability-to-pressure-solutions","Synthetic Darcy Flow Data Pipeline: From GRF Permeability to Pressure Solutions",[22,7472,7473,7474,7476],{},"The core skill taught is generating high-fidelity training data for operator learning on the 2D Darcy equation: -∇·(k∇u) = f over ",[77,7475,7402],{},"² with Dirichlet BCs u=0. Start with DarcyFlowDataGenerator(resolution=32, length_scale=0.15, variance=1.0). It builds a Gaussian Random Field (GRF) covariance matrix for permeability k(x,y) = exp(GRF), using exponential kernel exp(-dist²\u002F(2*length_scale²)) + jitter, Cholesky decomposed for efficient sampling: z ~ N(0,I), samples = L @ z.",[22,7478,7479,7480,7483,7484,7487,7488,7491,7492,7495,7496,7499],{},"Solve for pressure u using iterative Jacobi: for interior points, u",[77,7481,7482],{},"i,j"," = (k_e u",[77,7485,7486],{},"i,j+1"," + k_w u",[77,7489,7490],{},"i,j-1"," + k_n u",[77,7493,7494],{},"i-1,j"," + k_s u",[77,7497,7498],{},"i+1,j"," + dx² f) \u002F (k_e + k_w + k_n + k_s), converging in ~5000 steps or tol=1e-6. Generate n_samples=200 train\u002F50 test pairs. Wrap in PyTorch Dataset with channel dim and optional z-score normalization (store mean\u002Fstd for denorm). Use DataLoader(batch_size=16). Principle: GRF captures realistic heterogeneous permeability (e.g., subsurface flows); finite differences provide ground-truth without external solvers. Common mistake: Underdamped length_scale (>0.2) yields smooth k, poor generalization—use 0.1-0.15 for multiscale. Quality check: Visualize 3 samples side-by-side (viridis for k, hot for u) to confirm pressure pools in high-k regions.",[7501,7502,7505],"pre",{"className":7503,"code":7504,"language":201,"meta":178,"style":178},"language-python shiki shiki-themes github-light github-dark","# Key generation snippet\ngenerator = DarcyFlowDataGenerator(resolution=32, length_scale=0.15)\nperm_train, press_train = generator.generate_dataset(200)\n",[26,7506,7507,7514,7519],{"__ignoreMap":178},[77,7508,7511],{"class":7509,"line":7510},"line",1,[77,7512,7513],{},"# Key generation snippet\n",[77,7515,7516],{"class":7509,"line":179},[77,7517,7518],{},"generator = DarcyFlowDataGenerator(resolution=32, length_scale=0.15)\n",[77,7520,7521],{"class":7509,"line":7439},[77,7522,7523],{},"perm_train, press_train = generator.generate_dataset(200)\n",[17,7525,7527],{"id":7526},"fourier-neural-operator-spectral-kernels-for-resolution-independent-mapping","Fourier Neural Operator: Spectral Kernels for Resolution-Independent Mapping",[22,7529,7530],{},"FNO learns function-to-function operators k → u by parameterizing Fourier multipliers. Key blocks: SpectralConv2d(in_ch=1, out_ch=1, modes1=8, modes2=8) does FFT → low-freq multiply (weights ~1\u002F(in*out)) → iFFT; handles wraparound with dual weights for positive\u002Fnegative freqs. FNOBlock adds local Conv2d(1x1) residual + GELU. Full FourierNeuralOperator2D: lift k (32x32x1) + grid (x,y linspace 0-1) via Linear(3→width=32), pad=5, 4 FNOBlocks, unpad, project Linear(32→128→1). ~100k params. Forward: permute to NCHW, cat grid, process, return NC(1)HW.",[22,7532,7533],{},"Why spectral? Convolution = Fourier multiply; truncating high modes (modes=12 max for 64res) ignores noise, enables zero-shot super-res. Trade-off: Padding needed for FFT modes; fix via consistent pad\u002Funpad. Train with MSE on full fields (no points). Mistake: Forgetting grid encoding—FNOs are translation-equivariant but need pos for bounded domains. Eval: Relative L2 = ||u_pred - u|| \u002F ||u|| \u003C 1e-3 good for surrogates.",[7501,7535,7537],{"className":7503,"code":7536,"language":201,"meta":178,"style":178},"fno = FourierNeuralOperator2D(modes1=8, modes2=8, width=32, n_layers=4).to(device)\n# Forward: out = fno(perm_batch)  # learns k → u operator\n",[26,7538,7539,7544],{"__ignoreMap":178},[77,7540,7541],{"class":7509,"line":7510},[77,7542,7543],{},"fno = FourierNeuralOperator2D(modes1=8, modes2=8, width=32, n_layers=4).to(device)\n",[77,7545,7546],{"class":7509,"line":179},[77,7547,7548],{},"# Forward: out = fno(perm_batch)  # learns k → u operator\n",[17,7550,7552],{"id":7551},"physics-informed-nns-pde-residuals-without-full-data","Physics-Informed NNs: PDE Residuals Without Full Data",[22,7554,7555,7556,7559,7560,7563],{},"PINNs solve unsupervised via multi-task loss on sparse\u002Fno data. PINN_MLP(input_dim=3: x,y,k → u): Fourier embedding (sin\u002Fcos(2π B · ",[77,7557,7558],{},"x,y","), B fixed rand, 64 freqs) + k, then Tanh MLP ",[77,7561,7562],{},"256→128→...→1",", Xavier init. Loss (lambda_data=1, pde=1, bc=10): data MSE(u_pred, u_obs), PDE residual -k(u_xx + u_yy) -1 via dual autograd (grad(u,x)→u_x→u_xx), BC MSE(u_bc=0). Collocation: sample interior\u002Fpde\u002Fbc points uniformly.",[22,7565,7566],{},"Principle: Autodiff enforces physics everywhere; Fourier feats boost freq capture vs ReLU. Trade-off: Stiff losses (tune lambdas, start data>>physics); slower than data-driven (grad graph). Mistake: No requires_grad_(True) on coords or forgetting create_graph=True for Hessians. Quality: Balance losses \u003C1e-4 each; physics loss drops signal overfit.",[7501,7568,7570],{"className":7503,"code":7569,"language":201,"meta":178,"style":178},"pinn = PINN_MLP(hidden_dims=[128]*4, n_frequencies=64).to(device)\nloss_fn = DarcyPINNLoss()\n# Usage: losses = loss_fn(pinn, x_data,y_data,k_data,u_data, x_pde,...)\n",[26,7571,7572,7577,7582],{"__ignoreMap":178},[77,7573,7574],{"class":7509,"line":7510},[77,7575,7576],{},"pinn = PINN_MLP(hidden_dims=[128]*4, n_frequencies=64).to(device)\n",[77,7578,7579],{"class":7509,"line":179},[77,7580,7581],{},"loss_fn = DarcyPINNLoss()\n",[77,7583,7584],{"class":7509,"line":7439},[77,7585,7586],{},"# Usage: losses = loss_fn(pinn, x_data,y_data,k_data,u_data, x_pde,...)\n",[17,7588,7590],{"id":7589},"cnn-surrogate-baseline-and-inference-benchmarking","CNN Surrogate Baseline and Inference Benchmarking",[22,7592,7593],{},"Add convolutional surrogate: UNet-like with Conv2d blocks as baseline (not physics-aware). Train all (FNO\u002FPINN\u002FCNN) via Trainer: Adam(lr=1e-3), MSE\u002Fdata loss for supervised, full physics loss for PINN. Loop: train_epoch (zero_grad→pred→loss→backward→step), validate no_grad MSE, save best val state, CosineAnnealLR. Plot semilogy train\u002Fval curves.",[22,7595,7596],{},"Benchmark: Time 1000 inferences on test set (torch.no_grad(), sync). FNO fastest (spectral lift), CNN mid, PINN slowest (autodiff). Save torch.save(model.state_dict(), 'fno_darcy.pth'). Principle: Surrogates 1000x faster than FD solvers for repeated k. Trade-off: FNO best gen (res-invariant), PINN data-efficient but eval slow. Post-train: Denorm preds, L2\u002Frel err plots.",[7501,7598,7600],{"className":7503,"code":7599,"language":201,"meta":178,"style":178},"trainer = Trainer(fno, Adam(fno.parameters(),1e-3))\nhistory = trainer.train(train_loader, test_loader, 100)\n",[26,7601,7602,7607],{"__ignoreMap":178},[77,7603,7604],{"class":7509,"line":7510},[77,7605,7606],{},"trainer = Trainer(fno, Adam(fno.parameters(),1e-3))\n",[77,7608,7609],{"class":7509,"line":179},[77,7610,7611],{},"history = trainer.train(train_loader, test_loader, 100)\n",[22,7613,7614],{},"\"The Fourier Neural Operator (FNO) learns mappings between function spaces by parameterizing the integral kernel in Fourier space. Key insight: Convolution in physical space = multiplication in Fourier space.\"",[22,7616,7617],{},"\"Physics-Informed Neural Networks (PINNs) incorporate physical laws directly into the loss function... residual of the PDE at collocation points.\"",[22,7619,7620],{},"\"GRF for permeability: realistic heterogeneous fields critical for subsurface modeling—smooth k leads to trivial solutions.\"",[22,7622,7623],{},"\"Benchmark shows FNO at 50ms\u002Finference vs FD Jacobi 2s—key for real-time surrogates in optimization loops.\"",[22,7625,7626],{},"\"Fourier features in PINN: sine activations capture high freqs better than Tanh alone, converging 2x faster.\"",[7628,7629,7630],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":178,"searchDepth":179,"depth":179,"links":7632},[7633,7634,7635,7636],{"id":7469,"depth":179,"text":7470},{"id":7526,"depth":179,"text":7527},{"id":7551,"depth":179,"text":7552},{"id":7589,"depth":179,"text":7590},[185],{"content_references":7639,"triage":7644},[7640],{"type":7641,"title":7642,"url":7643,"context":7437},"tool","NVIDIA PhysicsNeMo","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fphysicsnemo",{"relevance":7440,"novelty":7439,"quality":7440,"actionability":7440,"composite":7645,"reasoning":7646},3.8,"Category: AI & LLMs. The article provides a detailed step-by-step guide on building surrogate models for Darcy flow using PhysicsNeMo, which directly addresses practical applications in AI engineering. It includes specific coding examples and techniques that can be implemented, making it actionable for developers looking to integrate AI into their projects.","\u002Fsummaries\u002F70fa59cd85bd7438-build-fno-pinn-surrogates-for-darcy-flow-with-phys-summary","2026-04-13 17:07:34","2026-04-13 17:53:26",{"title":7459,"description":178},{"loc":7647},"70fa59cd85bd7438","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F13\u002Fa-step-by-step-coding-tutorial-on-nvidia-physicsnemo-darcy-flow-fnos-pinns-surrogate-models-and-inference-benchmarking\u002F","summaries\u002F70fa59cd85bd7438-build-fno-pinn-surrogates-for-darcy-flow-with-phys-summary",[202,203,201,7657],"ai-tools","Step-by-step Colab guide: generate 2D Darcy datasets via GRF & finite differences, implement\u002Ftrain FNO operators and PINNs, add CNN baselines, benchmark inference speeds for fast physics surrogates.",[],"IPLxAt2cJRj6noXhMQUdc_wi_l1bco1LfSl-Q5gD5GI"]