机器学习-1
- ML Lecture 0-1 Introduction of Machine Learning
- ML Lecture2 Why we need to learn machine learning
- ML Lecture 1 Regression - Case Study
- ML Lecture 1 Regression - Demo
- ML Lecture 2 Where does the error come from
- ML Lecture 3-1 Gradient Descent
- ML Lecture 3-2 Gradient Descent (Demo by AOE)
- ML Lecture 3-3 Gradient Descent
- ML Lecture 4 Classification
- ML Lecture 5 Logistic Regression
- ML Lecture 6 Brief Introduction of Deep Learning
- ML Lecture 7 Backpropagation
- ML Lecture 8-1 “Hello world” of deep learning
- ML Lecture 8-2 Keras 2.0
- ML Lecture 8-3 Keras Demo
- ML Lecture 9-1 Tips for Training DNN
- ML Lecture 9-2 Keras Demo 2
- ML Lecture 9-3 Fizz Buzz in Tensorflow (sequel)
- ML Lecture 10 Convolutional Neural Network
- ML Lecture 11 Why Deep
- ML Lecture 12 Semi-supervised
- ML Lecture 13 Unsupervised Learning
- ML Lecture 14 Unsupervised Learning
- ML Lecture 15 Unsupervised Learning
- ML Lecture 16 Unsupervised Learning - Auto-encoder
- ML Lecture 17 Unsupervised Learning
- ML Lecture 18 Unsupervised Learning
- ML Lecture 19 Transfer Learning
- ML Lecture 20 Support Vector Machine (SVM)
- ML Lecture 21-1 Recurrent Neural Network (Part I)
- ML Lecture 21-2 Recurrent Neural Network (Part II)
- ML Lecture 22 Ensemble
- ML Lecture 23-1 Deep Reinforcement Learning
- ML Lecture 23-2 Policy Gradient
- ML Lecture 23-3 Reinforcement Learning
机器学习-2
- Anomaly Detection (1 7)
- Anomaly Detection (2 7) (2)
- Anomaly Detection (3 7)
- Anomaly Detection (4 7)
- Anomaly Detection (5 7)
- Anomaly Detection (6 7)
- Anomaly Detection (7 7)
- Attack ML Models (1 8)
- Attack ML Models (2 8)
- Attack ML Models (3 8)
- Attack ML Models (4 8)
- Attack ML Models (5 8)
- Attack ML Models (6 8)
- Attack ML Models (7 8)
- Attack ML Models (8 8)
- Explainable ML (1 8)
- Explainable ML (2 8)
- Explainable ML (3 8)
- Explainable ML (4 8)
- Explainable ML (5 8)
- Explainable ML (6 8)
- Explainable ML (7 8)
- Explainable ML (8 8)
- The Next Step for Machine Learning
高级机器学习
- Review Basic Structures for Deep Learning Models-1
- Review Basic Structures for Deep Learning Models-2
- Computational Graph & Backpropagation
- Deep Learning for Language Modeling
- Spatial Transformer Layer
- Highway Network & Grid LSTM
- Recursive Network
- Conditional Generation by RNN & Attention
- Pointer Network
- Batch Normalization
- SELU
- Tuning Hyperparameters
- Interesting things about deep learning
- Generative Adversarial Network
- Improved Generative Adversarial Network
- RL and GAN for Sentence Generation and Chat-bot
- 機械学習で美少女化 ~ あるいはNEW GAME! の世界
- Imitation Learning
- Evaluation of Generative Models
- Ensemble of GAN
- Energy-based GAN
- Video Generation by GAN
- A3C
- Gated RNN and Sequence Generation
深度学习理论
- Deep Learning Theory 1-1
- Deep Learning Theory 1-2 Potential of Deep
- Deep Learning Theory 1-3
- Deep Learning Theory 2-1 When Gradient is Zero
- Deep Learning Theory 2-2 Deep Linear Network
- Deep Learning Theory 2-3
- Deep Learning Theory 2-4
- Deep Learning Theory 2-5
- Deep Learning Theory 3-1
- Deep Learning Theory 3-2
深度强化学习
对抗生成网络GAN
- GAN Lecture 1 (2018) Introduction
- GAN Lecture 2 (2018) Conditional Generation
- GAN Lecture 3Unsupervised Conditional Generation
- GAN Lecture 4 (2018) Basic Theory
- GAN Lecture 5 (2018) General Framework
- GAN Lecture 6 (2018) WGAN, EBGAN
- GAN Lecture 7 (2018) Info GAN, VAE-GAN, BiGAN
- GAN Lecture 8 (2018) Photo Editing
- GAN Lecture 9 (2018) Sequence Generation
- GAN Lecture 10Evaluation & Concluding Remarks
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