├── .gitignore ├── LICENSE ├── Lec01_Overview of Tensorflow └── Lec01_Overview of Tensorflow.ipynb ├── Lec02_Operations └── Lec02_Operations.ipynb ├── Lec03_Linear and Logistic Regression ├── How to simply use tf.data.ipynb ├── Lec03_Linear Regression with huber loss by high-level.ipynb ├── Lec03_Linear Regression with huber loss by low-level.ipynb ├── Lec03_Linear Regression with mse loss.ipynb ├── Lec03_Linear Regression with tf.data.ipynb ├── Lec03_Linear Regression with tf.data_de.ipynb ├── Lec03_Logistic Regression with ce loss.ipynb ├── Lec03_Logistic Regression with tf.data.ipynb └── Lec03_Logistic Regression with tf.data_de.ipynb ├── Lec04_Eager execution ├── Lec04_Automatic differentiation and gradient tape.ipynb ├── Lec04_Custom training basics.ipynb ├── Lec04_Custom training subclassing.ipynb ├── Lec04_Custom training walkthrough.ipynb └── Lec04_Eager execution.ipynb ├── Lec05_Variable sharing and managing experiments ├── How to use keras.ipynb ├── Lec05_Applied example with tf.data.ipynb ├── Lec05_Applied example with tf.data_kde.ipynb ├── Lec05_Applied example with tf.placeholder.ipynb ├── Lec05_Randomization.ipynb ├── Lec05_Variable sharing.ipynb └── Lec05_Word2vec_simple.ipynb ├── Lec07_ConvNet in Tensorflow ├── Lec07_ConvNet mnist by high-level.ipynb ├── Lec07_ConvNet mnist by high-level_kd.ipynb ├── Lec07_ConvNet mnist by high-level_kde.ipynb ├── Lec07_ConvNet mnist by low-level.ipynb ├── Lec07_ConvNet mnist with Weight initialization and Batch norm.ipynb ├── Lec07_ConvNet mnist with Weight initialization and Batch norm_kd.ipynb ├── Lec07_ConvNet mnist with Weight initialization and Batch norm_kde.ipynb ├── Lec07_ConvNet mnist with Weight initialization and Drop out.ipynb └── Lec07_ConvNet mnist with Weight initialization and Drop out_kde.ipynb ├── Lec11_Recurrent Neural Networks ├── Lec11_Many to Many Classification by Bi-directional GRU.ipynb ├── Lec11_Many to Many Classification by Bi-directional LSTM.ipynb ├── Lec11_Many to Many Classification by Bi-directional RNN.ipynb ├── Lec11_Many to Many Classification by GRU.ipynb ├── Lec11_Many to Many Classification by LSTM.ipynb ├── Lec11_Many to Many Classification by RNN.ipynb ├── Lec11_Many to Many Classification by Stacked Bi-directional GRU with Drop out.ipynb ├── Lec11_Many to Many Classification by Stacked Bi-directional LSTM with Drop out.ipynb ├── Lec11_Many to Many Classification by Stacked Bi-directional RNN with Drop out.ipynb ├── Lec11_Many to Many Classification by Stacked GRU with Drop out.ipynb ├── Lec11_Many to Many Classification by Stacked LSTM with Drop out.ipynb ├── Lec11_Many to Many Classification by Stacked RNN with Drop out.ipynb ├── Lec11_Many to One Classification by Bi-directional GRU.ipynb ├── Lec11_Many to One Classification by Bi-directional LSTM.ipynb ├── Lec11_Many to One Classification by Bi-directional RNN.ipynb ├── Lec11_Many to One Classification by GRU.ipynb ├── Lec11_Many to One Classification by LSTM.ipynb ├── Lec11_Many to One Classification by RNN.ipynb ├── Lec11_Many to One Classification by RNN_kde.ipynb ├── Lec11_Many to One Classification by Stacked Bi-directional GRU with Drop out.ipynb ├── Lec11_Many to One Classification by Stacked Bi-directional LSTM with Drop out.ipynb ├── Lec11_Many to One Classification by Stacked Bi-directional RNN with Drop out.ipynb ├── Lec11_Many to One Classification by Stacked GRU with Drop out.ipynb ├── Lec11_Many to One Classification by Stacked LSTM with Drop out.ipynb ├── Lec11_Many to One Classification by Stacked RNN with Drop out.ipynb └── To quickly implementing RNN.ipynb ├── Lec12_Seq2Seq with Attention ├── Lec12_Seq2Seq by Encoder Bi-directional RNN and Decoder RNN.ipynb ├── Lec12_Seq2Seq by Encoder RNN and Decoder RNN.ipynb ├── Lec12_Seq2Seq with Attention by Encoder Bi-directional RNN and Decoder RNN.ipynb └── Lec12_Seq2Seq with Attention by Encoder RNN and Decoder RNN.ipynb ├── README.md ├── data ├── lecture03 │ ├── .DS_Store │ ├── example_with_data │ │ ├── .DS_Store │ │ ├── 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