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李宏毅2020机器学习深度学习(完整版)国语

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视频教程
人工智能
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算法
公开课
李宏毅
机器学习
深度学习
DEEPLEARNING
MACHINELEARNING
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视频选集
（5/119）
机器学习
37:08
Rule of ML 2020
22:04
Regression - Case Study
01:18:35
Basic Concept
43:14
Gradient Descent_1
01:01:52
Gradient Descent_2
02:36
Gradient Descent_3
01:41
Optimization for Deep Learning (1_2) (选学)
54:36
Optimization for Deep Learning (2_2) (选学)
54:32
Classification_1
01:09:41
Logistic Regression
01:07:13
Brief Introduction of Deep Learning
46:30
Backpropagation
31:26
Tips for Training DNN
01:26:02
Why Deep-
57:45
PyTorch Tutorial
52:39
Convolutional Neural Network
01:19:29
Graph Neural Network (1_2) (选学)
39:02
Graph Neural Network (2_2) (选学)
01:11:37
Recurrent Neural Network (Part I)
49:00
Recurrent Neural Network (Part II)
01:30:50
Semi-supervised
59:59
Unsupervised Learning - Word Embedding
40:39
Explainable ML (1_8)
13:51
Explainable ML (2_8)
14:08
Explainable ML (3_8)
06:08
Explainable ML (4_8)
07:16
Explainable ML (5_8)
08:13
Explainable ML (6_8)
07:27
Explainable ML (7_8)
08:03
Explainable ML (8_8)
07:17
More about Explainable AI (选学)
01:44:29
Attack ML Models (1_8)
06:06
Attack ML Models (2_8)
11:43
Attack ML Models (3_8)
07:03
Attack ML Models (4_8)
08:12
Attack ML Models (5_8)
06:39
Attack ML Models (6_8)
09:27
Attack ML Models (7_8)
08:04
Attack ML Models (8_8)
10:11
More about Adversarial Attack (1_2) (选学)
29:58
More about Adversarial Attack (2_2) (选学)
29:45
Network Compression (1_6)
08:24
Network Compression (2_6)
12:44
Network Compression (3_6)
08:33
Network Compression (4_6)
06:37
Network Compression (5_6)
11:53
Network Compression (6_6)
13:09
Network Compression (1_2) - Knowledge Distillation (选学)
01:07:53
Network Compression (2_2) - Network Pruning (选学)
39:33
Conditional Generation by RNN & Attention
01:41:14
Pointer Network
13:34
Recursive
24:58
Transformer
49:32
Transformer and its variant (选学)
01:49:23
Unsupervised Learning - Linear Methods
01:40:20
Unsupervised Learning - Neighbor Embedding
30:58
Unsupervised Learning - Auto-encoder
42:04
More about Auto-encoder (1_4)
13:39
More about Auto-encoder (2_4)
06:12
More about Auto-encoder (3_4)
12:29
More about Auto-encoder (4_4)
14:52
ELMO, BERT, GPT
01:04:52
Self-supervised Learning (选学)
40:37
Anomaly Detection (1_7)
13:23
Anomaly Detection (2_7)
14:10
Anomaly Detection (3_7)
14:05
Anomaly Detection (4_7)
04:08
Anomaly Detection (6_7)
12:20
Anomaly Detection (7_7)
06:02
More about Anomaly Detection (选学)
46:52
Generative Adversarial Network(1_10)
01:33:15
Generative Adversarial Network(2_10)
26:19
Generative Adversarial Network(3_10)
38:59
Generative Adversarial Network(4_10)
01:20:19
Generative Adversarial Network(5_10)
25:04
Generative Adversarial Network(6_10)
50:07
Generative Adversarial Network(7_10)
46:03
Generative Adversarial Network(8_10)
22:46
Generative Adversarial Network(9_10)
01:27:23
Generative Adversarial Network(10_10)
30:09
SAGAN, BigGAN, SinGAN, GauGAN, GANILLA, NICE-GAN（选学）
01:06:17
Transfer Learning
01:14:28
More about Domain Adaptation (1_2) (选学)
52:01
More about Domain Adaptation (2_2) (选学)
34:11
Meta Learning – MAML (1_9)
07:42
Meta Learning – MAML (2_9)
07:53
Meta Learning – MAML (3_9)
10:21
Meta Learning – MAML (4_9)
05:15
Meta Learning – MAML (5_9)
13:22
Meta Learning – MAML (6_9)
06:33
Meta Learning – MAML (7_9)
08:54
Meta Learning – MAML (8_9)
05:12
Meta Learning – MAML (9_9)
06:53
Meta Learning - Gradient Descent as LSTM (1_3)
11:40
Meta Learning - Gradient Descent as LSTM (2_3)
10:06
Meta Learning - Gradient Descent as LSTM (3_3)
10:39
More about Meta Learning (选学)
44:10
More about Meta Learning (选学)
53:11
Life Long Learning (1_7)
13:51
Life Long Learning (2_7)
07:25
Life Long Learning (3_7)
12:04
Life Long Learning (4_7)
04:40
Life Long Learning (5_7)
03:20
Life Long Learning (6_7)
14:15
Life Long Learning (7_7)
11:35
More about Lifelong Learning (选学)
57:48
Deep Reinforcemen Learning(3_1)
01:06:22
Deep Reinforcemen Learning(3_2)
13:20
Deep Reinforcemen Learning(3_3)
01:05:34
RL Advanced Version_1_Policy Gradient
45:49
RL Advanced Version_2_ Proximal Policy Optimization
41:34
RL Advanced Version_3_Q-Learning
49:44
RL Advanced Version_4_Q-Learning Advanced Tips
38:31
RL Advanced Version_5_Q-Learning Continuous Action
14:58
RL Advanced Version_6_Actor-Critic
34:16
RL Advanced Version_7_Sparse Reward
30:16
RL Advanced Version_8_Imitation Learning
34:02
RL - Model-based, Large-scale, Meta, Multi-agent, Hide-and-seek, Alpha（选学）
01:42:05

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