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Aug. 4, 2017
Created
Apr. 2, 2017

Machine Learning with TensorFlow(R version)

This is the unofficial code repository for Machine Learning with TensorFlow with R.

This repository is for practicing (R tensorflow)[https://cran.r-project.org/package=tensorflow] modeling exercises. I'm personally writing this code for me to know that there are some areas where you can get the benefits of R, and that the code in the book may contain partially improved or experimented code. (example: CNN model view )

TODO

  • make full boook example code with R.
  • make use of R Reference Class for code reusablilty.
  • adding GAN code.

Requirement

Summary

Chapter 2 - TensorFlow Basics

  • Concept 1: Defining tensors
  • Concept 2: Evaluating ops
  • Concept 3: Interactive session
  • Concept 4: Session loggings
  • Concept 5: Variables
  • Concept 6: Saving variables
  • Concept 7: Loading variables
  • Concept 8: TensorBoard

Chapter 3 - Regression

  • Concept 1: Linear regression
  • Concept 2: Polynomial regression
  • Concept 3: Regularization

Chapter 4 - Classification

  • Concept 1: Linear regression for classification
  • Concept 2: Logistic regression
  • Concept 3: 2D Logistic regression
  • Concept 4: Softmax classification

Chapter 5 - Clustering (working)

  • Concept 1: Clustering
  • Concept 2: Segmentation
  • Concept 3: Self-organizing map

Chapter 6 - Hidden markov models

  • Concept 1: Forward algorithm
  • Concept 2: Viterbi decode

Chapter 7 - Autoencoders (working)

  • Concept 1: Autoencoder
  • Concept 2: Applying an autoencoder to images
  • Concept 3: Denoising autoencoder

Chapter 8 - Reinforcement learning (working)

  • Concept 1: Reinforcement learning

Chapter 9 - Convolutional Neural Networks

  • Concept 1: Using CIFAR-10 dataset
  • Concept 2: Convolutions
  • Concept 3: Convolutional neural network
  • Concept 4: Convolutional neural network model debugging(2), Newly added

Chapter 10 - Recurrent Neural Network(working)

  • Concept 1: Loading timeseries data
  • Concept 2: Recurrent neural networks
  • Concept 3: Applying RNN to real-world data for timeseries prediction