Machine Learning with TensorFlow

 

https://github.com/BinRoot/TensorFlow-Book?utm_content=buffer3f3de&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

 

 

 

Machine Learning with TensorFlow

This is the official code repository for Machine Learning with TensorFlow.

Warning: The book will be released in a month or two, so this repo is a pre-release of the entire code. I will be heavily updating this repo in the coming weeks. Stay tuned, and follow along! :)

Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.

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

• 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

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

Chapter 8 - Reinforcement learning

• Concept 1: Reinforcement learning

Chapter 9 - Convolutional Neural Networks

• Concept 1: Using CIFAR-10 dataset
• Concept 2: Convolutions
• Concept 3: Convolutional neural network

Chapter 10 - Recurrent Neural Network

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