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"Deep Learning Techniques and Applications" ,Ge Li ,Graduated Course ,Peking University

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Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
01 - Introduction of Deep Learning.pdf		01 - Introduction of Deep Learning.pdf
02 - Back Propagation Algorithm.pdf		02 - Back Propagation Algorithm.pdf
03 - Training Optimization Algorithms.pdf		03 - Training Optimization Algorithms.pdf
04 - Training Methods & Learning Process.pdf		04 - Training Methods & Learning Process.pdf
05 - Activation Functions.pdf		05 - Activation Functions.pdf
06 - Loss Functions.pdf		06 - Loss Functions.pdf
07 - Parameter Initialization Methods.pdf		07 - Parameter Initialization Methods.pdf
08 - Learning Rate Optimization Methods.pdf		08 - Learning Rate Optimization Methods.pdf
09 - Regularization & Dropout.pdf		09 - Regularization & Dropout.pdf
10 - Data Preprocessing & Normalization.pdf		10 - Data Preprocessing & Normalization.pdf
11 - Covolutional Neural Network I.pdf		11 - Covolutional Neural Network I.pdf
12 - Convolutional Neural Network II.pdf		12 - Convolutional Neural Network II.pdf
13 - Recurrent Neural Network & LSTM.pdf		13 - Recurrent Neural Network & LSTM.pdf
14 - Encoder-Decoder & Attention Machanism.pdf		14 - Encoder-Decoder & Attention Machanism.pdf
15 - AutoEnder & Stacked AutoEncoder.pdf		15 - AutoEnder & Stacked AutoEncoder.pdf
Deep Learning Book -Full.pdf		Deep Learning Book -Full.pdf
README.md		README.md