https://github.com/binroot/tensorflow-book

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
https://github.com/binroot/tensorflow-book

autoencoder book classification clustering convolutional-neural-networks linear-regression logistic-regression machine-learning regression reinforcement-learning tensorflow

Last synced: 25 days ago
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Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

• Host: GitHub
• URL: https://github.com/binroot/tensorflow-book
• Owner: BinRoot
• License: mit
• Created: 2016-03-06T23:30:57.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2023-03-17T17:59:01.000Z (over 1 year ago)
• Last Synced: 2024-10-14T21:22:01.075Z (25 days ago)
• Topics: autoencoder, book, classification, clustering, convolutional-neural-networks, linear-regression, logistic-regression, machine-learning, regression, reinforcement-learning, tensorflow
• Language: Jupyter Notebook
• Homepage: http://www.tensorflowbook.com
• Size: 43.6 MB
• Stars: 4,453
• Watchers: 297
• Forks: 1,206
• Open Issues: 15
• Metadata Files:
  • Readme: README.md

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README

        
# [Machine Learning with TensorFlow](http://www.tensorflowbook.com/)

[This](https://github.com/BinRoot/TensorFlow-Book) is the official code repository for [Machine Learning with TensorFlow](http://www.tensorflowbook.com/).

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

# Summary

## [Chapter 2](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch02_basics) - 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](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch03_regression) - Regression

- **Concept 1**: Linear regression

- **Concept 2**: Polynomial regression

- **Concept 3**: Regularization

## [Chapter 4](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch04_classification) - Classification

- **Concept 1**: Linear regression for classification

- **Concept 2**: Logistic regression

- **Concept 3**: 2D Logistic regression

- **Concept 4**: Softmax classification

## [Chapter 5](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch05_clustering) - Clustering

- **Concept 1**: Clustering

- **Concept 2**: Segmentation

- **Concept 3**: Self-organizing map

## [Chapter 6](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch06_hmm) - Hidden markov models

- **Concept 1**: Forward algorithm

- **Concept 2**: Viterbi decode

## [Chapter 7](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch07_autoencoder) - Autoencoders

- **Concept 1**: Autoencoder

- **Concept 2**: Applying an autoencoder to images

- **Concept 3**: Denoising autoencoder

## [Chapter 8](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch08_rl) - Reinforcement learning

- **Concept 1**: Reinforcement learning

## [Chapter 9](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch09_cnn) - Convolutional Neural Networks

- **Concept 1**: Using CIFAR-10 dataset

- **Concept 2**: Convolutions

- **Concept 3**: Convolutional neural network

## [Chapter 10](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch10_rnn) - Recurrent Neural Network

- **Concept 1**: Loading timeseries data

- **Concept 2**: Recurrent neural networks

- **Concept 3**: Applying RNN to real-world data for timeseries prediction

## [Chapter 11](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch11_seq2seq) - Seq2Seq Model

- **Concept 1**: Multi-cell RNN

- **Concept 2**: Embedding lookup

- **Concept 3**: Seq2seq model

## [Chapter 12](https://github.com/BinRoot/TensorFlow-Book/tree/master/ch12_rank) - Ranking

- **Concept 1**: RankNet

- **Concept 2**: Image embedding

- **Concept 3**: Image ranking