机器学习2017,机器学习2019,高级机器学习
深度学习理论
深度强化学习
对抗生成网络GAN
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- 第一章：机器学习-1
- ML Lecture 1 Regression - Case Study 免费 01:18:34
- ML Lecture 1 Regression - Demo 免费 06:52
- ML Lecture 3-1 Gradient Descent 免费 01:01:51
- ML Lecture 3-3 Gradient Descent 免费 01:40
- ML Lecture 4 Classification 免费 01:09:40
- ML Lecture 5 Logistic Regression 免费 01:07:13
- ML Lecture 7 Backpropagation 免费 31:25
- ML Lecture 8-2 Keras 2.0 免费 09:37
- ML Lecture 8-3 Keras Demo 免费 11:12
- ML Lecture 9-1 Tips for Training DNN 免费 01:26:02
- ML Lecture 9-2 Keras Demo 2 免费 15:20
- ML Lecture 10 Convolutional Neural Network 免费 01:19:28
- ML Lecture 11 Why Deep 免费 57:44
- ML Lecture 12 Semi-supervised 免费 59:59
- ML Lecture 13 Unsupervised Learning 免费 01:40:20
- ML Lecture 14 Unsupervised Learning 免费 40:38
- ML Lecture 15 Unsupervised Learning 免费 30:58
- ML Lecture 17 Unsupervised Learning 免费 29:33
- ML Lecture 18 Unsupervised Learning 免费 01:03:30
- ML Lecture 19 Transfer Learning 免费 01:14:27
- ML Lecture 20 Support Vector Machine (SVM) 免费 01:05:26
- ML Lecture 22 Ensemble 免费 01:39:58
- ML Lecture 23-1 Deep Reinforcement Learning 免费 01:06:21
- ML Lecture 23-2 Policy Gradient 免费 13:19
- ML Lecture 23-3 Reinforcement Learning 免费 01:05:33
- 第二章：机器学习-2
- Anomaly Detection (1 7) 免费 13:22
- Anomaly Detection (2 7) (2) 免费 14:08
- Anomaly Detection (3 7) 免费 14:04
- Anomaly Detection (4 7) 免费 04:06
- Anomaly Detection (5 7) 免费 12:17
- Anomaly Detection (6 7) 免费 12:18
- Anomaly Detection (7 7) 免费 06:01
- Attack ML Models (1 8) 免费 06:05
- Attack ML Models (2 8) 免费 11:41
- Attack ML Models (3 8) 免费 07:02
- Attack ML Models (4 8) 免费 08:10
- Attack ML Models (5 8) 免费 06:37
- Attack ML Models (6 8) 免费 09:25
- Attack ML Models (7 8) 免费 08:02
- Attack ML Models (8 8) 免费 10:10
- Explainable ML (1 8) 免费 13:50
- Explainable ML (2 8) 免费 14:07
- Explainable ML (3 8) 免费 06:06
- Explainable ML (4 8) 免费 07:14
- Explainable ML (5 8) 免费 08:11
- Explainable ML (6 8) 免费 07:26
- Explainable ML (7 8) 免费 08:02
- Explainable ML (8 8) 免费 07:15
- The Next Step for Machine Learning 免费 15:26
- 第三章：高级机器学习
- Computational Graph & Backpropagation 免费 01:00:53
- Deep Learning for Language Modeling 免费 29:57
- Spatial Transformer Layer 免费 32:55
- Highway Network & Grid LSTM 免费 18:21
- Recursive Network 免费 24:57
- Conditional Generation by RNN & Attention 免费 01:41:13
- Pointer Network 免费 13:33
- Batch Normalization 免费 28:57
- SELU 免费 25:57
- Tuning Hyperparameters 免费 19:36
- Generative Adversarial Network 免费 01:59:03
- Improved Generative Adversarial Network 免费 02:22:48
- RL and GAN for Sentence Generation and Chat-bot 免费 01:29:09
- 機械学習で美少女化 ~ あるいはNEW GAME! の世界 免费 10:35
- Imitation Learning 免费 01:22:04
- Evaluation of Generative Models 免费 23:47
- Ensemble of GAN 免费 55:35
- Energy-based GAN 免费 08:15
- Video Generation by GAN 免费 06:22
- A3C 免费 01:32:27
- Gated RNN and Sequence Generation 免费 01:59:51
- 第四章：深度学习理论
- Deep Learning Theory 1-1 免费 36:48
- Deep Learning Theory 1-3 免费 26:17
- Deep Learning Theory 2-1 When Gradient is Zero 免费 01:17:28
- Deep Learning Theory 2-3 免费 17:39
- Deep Learning Theory 2-4 免费 17:05
- Deep Learning Theory 2-5 免费 38:45
- Deep Learning Theory 3-1 免费 25:37
- Deep Learning Theory 3-2 免费 42:55
- 第五章：深度强化学习
- DRL Lecture 6 Actor-Critic 免费 34:15
- DRL Lecture 7 Sparse Reward 免费 30:15
- DRL Lecture 8 Imitation Learning 免费 34:01
- 第六章：对抗生成网络GAN
- GAN Lecture 1 (2018) Introduction 免费 01:33:14
- GAN Lecture 4 (2018) Basic Theory 免费 01:20:18
- GAN Lecture 6 (2018) WGAN, EBGAN 免费 50:06
- GAN Lecture 8 (2018) Photo Editing 免费 22:45
- GAN Lecture 9 (2018) Sequence Generation 免费 01:27:22
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课程介绍
李宏毅（（Hung-yi Lee）目前任台湾大学电机工程学系和电机资讯学院的助理教授，他曾于2012年获得台湾大学博士学位，并于2013年赴麻省理工学院（MIT）计算机科学和人工智能实验室做访问学者。他的研究方向主要是语义理解、语音识别、机器学习和深度学习）老师的机器学习课程以机器学习和深度学习的基础知识为主，包含了AI领域的各种最新知识和技术点。课程深入简出，通俗易懂，颇受欢迎，非常适合对AI感兴趣的学习者。本课程已经获得李宏毅老师授权。
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