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 )
- make full boook example code with R.
- make use of
R Reference Classfor code reusablilty. - adding GAN code.
- TensorFlow (>= 1.0)
- Python (>= 3.4)
- tensorflow (>= 0.7)
- reticulate (>= 0.7)
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