README

Machine Learning with TensorFlow(R version)

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

This repository may contain different experiment R code.

이 저장소는 R 텐서플로기반 텐서플로 모형작성 연습을 하기 위한 곳입니다. R만의 장점을 가질수 있는 부분이 있음을 개인적으로 코드작성을 통해 알아기기 위한 목적으로 작성되었으며, 책의 코드에서 부분적으로 개선되거나 실험된 코드/메모가 포함될 수 있음을 알려드립니다.(예: CNN model view )

TODO

• 예제 코드 완성
• R Reference Class 기반 코드 재활용성 추구
• GAN 모듈 추가

Requirement

• TensorFlow (> 1.0)
• Python (> 3.4)
• tensorflow
• reticulate

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