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ML Lecture 8-2: Keras 2.0
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Classroom Contents
Machine Learning
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- 1 ML Lecture 0-1: Introduction of Machine Learning
- 2 ML Lecture 0-2: Why we need to learn machine learning?
- 3 ML Lecture 1: Regression - Case Study
- 4 ML Lecture 1: Regression - Demo
- 5 ML Lecture 2: Where does the error come from?
- 6 ML Lecture 3-1: Gradient Descent
- 7 ML Lecture 3-2: Gradient Descent (Demo by AOE)
- 8 ML Lecture 3-3: Gradient Descent (Demo by Minecraft)
- 9 ML Lecture 4: Classification
- 10 ML Lecture 5: Logistic Regression
- 11 ML Lecture 6: Brief Introduction of Deep Learning
- 12 ML Lecture 7: Backpropagation
- 13 ML Lecture 8-1: “Hello world” of deep learning
- 14 ML Lecture 8-2: Keras 2.0
- 15 ML Lecture 8-3: Keras Demo
- 16 ML Lecture 9-1: Tips for Training DNN
- 17 ML Lecture 9-2: Keras Demo 2
- 18 ML Lecture 9-3: Fizz Buzz in Tensorflow (sequel)
- 19 ML Lecture 10: Convolutional Neural Network
- 20 ML Lecture 11: Why Deep?
- 21 ML Lecture 12: Semi-supervised
- 22 ML Lecture 13: Unsupervised Learning - Linear Methods
- 23 ML Lecture 14: Unsupervised Learning - Word Embedding
- 24 ML Lecture 15: Unsupervised Learning - Neighbor Embedding
- 25 ML Lecture 16: Unsupervised Learning - Auto-encoder
- 26 ML Lecture 17: Unsupervised Learning - Deep Generative Model (Part I)
- 27 ML Lecture 18: Unsupervised Learning - Deep Generative Model (Part II)
- 28 ML Lecture 19: Transfer Learning
- 29 ML Lecture 20: Support Vector Machine (SVM)
- 30 ML Lecture 21-1: Recurrent Neural Network (Part I)
- 31 ML Lecture 21-2: Recurrent Neural Network (Part II)
- 32 ML Lecture 22: Ensemble
- 33 ML Lecture 23-1: Deep Reinforcement Learning
- 34 ML Lecture 23-2: Policy Gradient (Supplementary Explanation)
- 35 ML Lecture 23-3: Reinforcement Learning (including Q-learning)
- 36 ML Lecture 21-1: Recurrent Neural Network (Part I) English version
