├── LICENSE ├── ML-notes-html ├── 10_Keras.html ├── 11_Convolutional-Neural-Network-part1.html ├── 12_Convolutional-Neural-Network-part2.html ├── 13_Tips-for-Deep-Learning.html ├── 14_Why-Deep.html ├── 15_Semi-supervised-Learning.html ├── 16_Unsupervised-Learning-Introduction.html ├── 17_Unsupervised-Learning-PCA-part1.html ├── 18_Unsupervised-Learning-PCA-part2.html ├── 19_Matrix-Factorization.html ├── 1_Introduction.html ├── 20_Unsupervised-Learning-Word-Embedding.html ├── 21_Unsupervised-Learning-Neighbor-Embedding.html ├── 22_Unsupervised-Learning-Deep-Auto-encoder.html ├── 23_Unsupervised-Learning-Generation.html ├── 24_Transfer-Learning.html ├── 25_Support-Vector-Machine.html ├── 26_Recurrent-Neural-Network-part1.html ├── 27_Recurrent-Neural-Network-part2.html ├── 2_Regression-Case-Study.html ├── 3_Regression-demo(Adagrad).html ├── 4_Where-does-the-error-come-from.html ├── 5_Gradient-Descent.html ├── 6_Classification.html ├── 7_Logistic-Regression.html ├── 8_Deep-Learning.html └── 9_Backpropagation.html ├── ML-notes-md ├── 10_Keras.md ├── 11_Convolutional Neural Network part1.md ├── 12_Convolutional Neural Network part2.md ├── 13_Tips for Deep Learning.md ├── 14_Why Deep.md ├── 15_Semi-supervised Learning.md ├── 16_Unsupervised Learning Introduction.md ├── 17_Unsupervised Learning PCA part1.md ├── 18_Unsupervised Learning PCA part2.md ├── 19_Matrix Factorization.md ├── 1_Introduction.md ├── 20_Unsupervised Learning Word Embedding.md ├── 21_Unsupervised Learning Neighbor Embedding.md ├── 22_Unsupervised Learning Deep Auto-encoder.md ├── 23_Unsupervised Learning Generation.md ├── 24_Transfer Learning.md ├── 25_Support Vector Machine.md ├── 26_Recurrent Neural Network part1.md ├── 27_Recurrent Neural Network part2.md ├── 2_Regression Case Study.md ├── 3_Regression demo(Adagrad).md ├── 4_Where does the error come from.md ├── 5_Gradient Descent.md ├── 6_Classification.md ├── 7_Logistic Regression.md ├── 8_Deep Learning.md └── 9_Backpropagation.md ├── ML-notes-pdf ├── 1_Introduction.pdf ├── 2_Regression Case Study.pdf ├── 3_Regression demo(Adagrad).pdf ├── 4_Where does the error come from.pdf ├── 5_Gradient Descent.pdf └── readme.md ├── README.md ├── code ├── Digits-Detection │ ├── digits-detection-cnn.py │ └── digits-detection.py ├── Gradient-Descent-Demo │ ├── Gradient-Descent-Demo.html │ ├── Gradient-Descent-Demo.ipynb │ └── GradientDescentDemo_official.ipynb └── pytorch │ ├── PyTorch_Introduction.ipynb │ ├── pytorch-introduction.md │ └── pytorch-tips.md ├── img ├── 5000-tests.png ├── L1-L2.png ├── L1L2regularization.png ├── Xcp-2.png ├── Xcp-3.png ├── Xcp-4.png ├── Xcp-5.png ├── Xcp-compare.png ├── Xcp-overfitting.png ├── best-function.png ├── bias-variance.png ├── bias-vs-variance.png ├── cross-validation.png ├── ds.png ├── estimator.png ├── goodness-of-function.png ├── gradient-descent.png ├── gradient-stuck.png ├── gradient-two-parameters.png ├── hidden-factors.png ├── large-bias.png ├── large-variance.png ├── learningMap.png ├── 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