All about reinforcement learning

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2019-09-28 05:45:06
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劝君更尽一杯酒 与尔同销万古愁
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CS294-112 9_25_17-yap_g0d7iBQ
01:20:32
CS294-112 9_27_17-AwdauFLan7M
01:12:16
CS294-112 10_2_17-vRkIwM4GktE
01:12:59
CS294-112 10_4_17-iOYiPhu5GEk
01:15:59
CS294-112 10_9_17--3BcZwgmZLk
01:14:32
CS294-112 10_11_17-ycCtmp4hcUs
01:20:11
CS294-112 10_16_17-npi6B4VQ-7s
01:21:37
CS294-112 10_18_17-0WbVUvKJpg4
01:18:40
CS294-112 10_23_17-UqSx23W9RYE
01:08:36
CS294-112 10_25_17-Xe9bktyYB34
01:16:15
CS294-112 10_30_17-mc-DtbhhiKA
01:22:35
CS294-112 11_1_17-j9QI21xtqV4
01:23:14
CS294-112 11_6_17-QJpc_T65QRY
01:23:28
CS294-112 11_8_17-CHKSBEx_k54
01:17:01
CS294-112 11_15_17-ixtEeS6aCKU
01:13:26
CS294-112 11_20_17-gqX8J38tESw
01:16:44
ICML 2017 - Test of Time Award (Sylvain Gelly & David Silver)-Bm7zah_LrmE
36:34
Lecture 1 _ Introduction to Convolutional Neural Networks for Visual Recognition
57:57
Lecture 1 _ Introduction to Convolutional Neural Networks for Visual Recognition
57:57
Lecture 2 _ Image Classification-OoUX-nOEjG0(1)
59:32
Lecture 2 _ Image Classification-OoUX-nOEjG0
59:32
Lecture 3 _ Loss Functions and Optimization-h7iBpEHGVNc
01:14:41
Lecture 4 _ Introduction to Neural Networks-d14TUNcbn1k
01:13:59
Lecture 5 _ Convolutional Neural Networks-bNb2fEVKeEo
01:08:57
Lecture 6 _ Training Neural Networks I-wEoyxE0GP2M
01:20:20
Lecture 7 _ Training Neural Networks II-_JB0AO7QxSA
01:15:30
Lecture 8 _ Deep Learning Software-6SlgtELqOWc
01:18:08
Lecture 9 _ CNN Architectures-DAOcjicFr1Y
01:17:40
Lecture 10 _ Recurrent Neural Networks-6niqTuYFZLQ
01:13:09
Lecture 11 _ Detection and Segmentation-nDPWywWRIRo
01:14:27
Lecture 12 _ Visualizing and Understanding-6wcs6szJWMY
01:15:48
Lecture 13 _ Generative Models-5WoItGTWV54
01:17:42
Lecture 14 _ Deep Reinforcement Learning-lvoHnicueoE
01:04:01
Lecture 15 _ Efficient Methods and Hardware for Deep Learning-eZdOkDtYMoo
01:16:53
Lecture 16 _ Adversarial Examples and Adversarial Training-CIfsB_EYsVI
01:21:46
RL Course by David Silver - Lecture 1 - Introduction to Reinforcement Learning-2
01:28:13
RL Course by David Silver - Lecture 2 - Markov Decision Process-lfHX2hHRMVQ
01:42:05
RL Course by David Silver - Lecture 3 - Planning by Dynamic Programming-Nd1-UUMV
01:39:09
RL Course by David Silver - Lecture 4 - Model-Free Prediction-PnHCvfgC_ZA
01:37:02
RL Course by David Silver - Lecture 5 - Model Free Control-0g4j2k_Ggc4
01:36:31
RL Course by David Silver - Lecture 6 - Value Function Approximation-UoPei5o4fps
01:36:45
RL Course by David Silver - Lecture 7 - Policy Gradient Methods-KHZVXao4qXs
01:33:58
RL Course by David Silver - Lecture 8 - Integrating Learning and Planning-ItMutb
01:40:13
RL Course by David Silver - Lecture 9 - Exploration and Exploitation-sGuiWX07sKw
01:39:18
RL Course by David Silver - Lecture 10 - Classic Games-kZ_AUmFcZtk
01:51:24
CS294-112 8_23_17-Q4kF8sfggoI
58:44
CS294-112 8_28_17-C_LGsoe36I8
01:22:38
CS294-112 8_30_17-PTbxa6GsTWc
01:19:18
CS294-112 9_6_17-tWNpiNzWuO8
01:09:42
CS294-112 9_11_17-PpVhtJn-iZI
01:16:58
CS294-112 9_13_17-k1vNh4rNYec
01:07:11
CS294-112 9_18_17-nZXC5OdDfs4
01:18:04
CS294-112 9_20_17-EfgC7v5V608
01:14:37
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