CS 20 : Tensorflow for Deep Learning Research

CS 20 : Tensorflow for Deep Learning Research의 Lecture 별 코드를 tensorflow 1.8.0에 맞게 정리

• syllabus : http://web.stanford.edu/class/cs20si/syllabus.html
• github : https://github.com/chiphuyen/stanford-tensorflow-tutorials

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01. Overview of Tensorflow

• Lec01 Overview of Tensorflow의 example code
  • Lec01_Overview of Tensorflow.ipynb

02. Operations

• Lec02 Operations의 example code
  • Lec02_Operations.ipynb

03. Linear and Logistic Regression

• Simple usage of tf.data

  • How to simply use tf.data.ipynb

• Lec03 Linear and Logistic Regression의 example code

  • Lec03_Linear Regression with mse loss.ipynb
  • Lec03_Linear Regression with huber loss by low-level.ipynb
  • Lec03_Linear Regression with huber loss by high-level.ipynb
  • Lec03_Linear Regression with tf.data.ipynb
  • Lec03_Logistic Regression with ce loss.ipynb
  • Lec03_Logistic Regression with tf.data.ipynb

04. Eager Execution

05. Variable sharing and managing experiments

• Lec05 Variable sharing and managing experiments의 example code
  • Lec05_Variable sharing.ipynb
  • Lec05_Randomization.ipynb
  • Lec05_Applied example with tf.placeholder.ipynb
  • Lec05_Applied example with tf.data.ipynb
  • Lec05_Word2vec_simple.ipynb

06. Introduction to ConvNet

07. ConvNet in TensorFlow

• Lec07 ConvNet in TensorFlow의 example code
  • Lec07_ConvNet mnist by low-level.ipynb
  • Lec07_ConvNet mnist by high-level.ipynb
  • Lec07_ConvNet mnist with Weight initialization and Drop out.ipynb
  • Lec07_ConvNet mnist with Weight initialization and Batch norm.ipynb

08. Style Transfer

09. Variational Auto-Encoders

10. Generative Adversarial Networks

11. Recurrent Neural Networks

• Presentation

  • To quickly implementing RNN.ipynb

• Lec11 Recurrent Neural Networks의 example code

• many to one, word sentiment classification example

  • Lec11_Sequence Classificaion by RNN.ipynb
  • Lec11_Sequence Classificaion by LSTM.ipynb
  • Lec11_Sequence Classificaion by GRU.ipynb
  • Lec11_Sequence Classificaion by Bi-directional RNN.ipynb
  • Lec11_Sequence Classificaion by Bi-directional LSTM.ipynb
  • Lec11_Sequence Classificaion by Bi-directional GRU.ipynb
  • Lec11_Sequence Classificaion by Stacked RNN with Drop out.ipynb
  • Lec11_Sequence Classificaion by Stacked LSTM with Drop out.ipynb
  • Lec11_Sequence Classificaion by Stacked GRU with Drop out.ipynb
  • Lec11_Sequence Classificaion by Stacked Bi-directional RNN with Drop out.ipynb
  • Lec11_Sequence Classificaion by Stacked Bi-directional LSTM with Drop out.ipynb
  • Lec11_Sequence Classificaion by Stacked Bi-directional GRU with Drop out.ipynb

• many to many, simple pos-tagger example

  • Lec11_Sequence to Sequence Classification by RNN.ipynb
  • Lec11_Sequence to Sequence Classification by LSTM.ipynb
  • Lec11_Sequence to Sequence Classification by GRU.ipynb
  • Lec11_Sequence to Sequence Classification by Bi-directional RNN.ipynb
  • Lec11_Sequence to Sequence Classification by Bi-directional LSTM.ipynb
  • Lec11_Sequence to Sequence Classification by Bi-directional GRU.ipynb
  • Lec11_Sequence to Sequence Classification by Stacked RNN with Drop out.ipynb
  • Lec11_Sequence to Sequence Classification by Stacked LSTM with Drop out.ipynb
  • Lec11_Sequence to Sequence Classification by Stacked GRU with Drop out.ipynb
  • Lec11_Sequence to Sequence Classification by Stacked Bi-directional RNN with Drop out.ipynb
  • Lec11_Sequence to Sequence Classification by Stacked Bi-directional LSTM with Drop out.ipynb
  • Lec11_Sequence to Sequence Classification by Stacked Bi-directional GRU with Drop out.ipynb

12. Seq2Seq with Attention

• Lec12 Seq2Seq with Attention의 example code

• encoder decoder (many to many), simple neural machine translation example

  • Lec12_Seq2Seq by Encoder RNN and Decoder RNN.ipynb
  • Lec12_Seq2Seq by Encoder Bi-directional RNN and Decoder RNN.ipynb
  • Lec12_Seq2Seq with Attention by Encoder RNN and Decoder RNN.ipynb