TensorFlow – Consise Examples for Beginners

README.md

TensorFlow Tutorial with popular machine learning algorithms implementation. This tutorial was designed for easily diving into TensorFlow, through examples.

It is suitable for beginners who want to find clear and concise examples about TensorFlow. For readability, the tutorial includes both notebook and code with explanations.

Tutorial index

1 – Introduction

2 – Basic Models

3 – Neural Networks

4 – Utilities

  • Save and Restore a model (notebook) (code)
  • Tensorboard – Graph and loss visualization (notebook) (code)
  • Tensorboard – Advanced visualization (code)

5 – Multi GPU

More Examples

The following examples are coming from TFLearn, a library that provides a simplified interface for TensorFlow. You can have a look, there are many examples and pre-built operations and layers.

Basics

Extending Tensorflow

  • Layers. Use TFLearn layers along with Tensorflow.
  • Trainer. Use TFLearn trainer class to train any Tensorflow graph.
  • Built-in Ops. Use TFLearn built-in operations along with Tensorflow.
  • Summaries. Use TFLearn summarizers along with Tensorflow.
  • Variables. Use TFLearn variables along with Tensorflow.

Computer Vision

Natural Language Processing

Notebooks

Dependencies

tensorflow
numpy
matplotlib
cuda
tflearn (if using tflearn examples)

For more details about TensorFlow installation, you can check TensorFlow Installation Guide

Dataset

Some examples require MNIST dataset for training and testing. Don’t worry, this dataset will automatically be downloaded when running examples (with input_data.py).
MNIST is a database of handwritten digits, with 60,000 examples for training and 10,000 examples for testing. (Website: http://yann.lecun.com/exdb/mnist/)


Original URL: http://feedproxy.google.com/~r/feedsapi/BwPx/~3/Kx6IaOVIpG0/TensorFlow-Examples

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