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.
Note: If you are using older TensorFlow version (before 0.12), please have a look here
0 - Prerequisite
- Introduction to Machine Learning (notebook)
- Introduction to MNIST Dataset (notebook)
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
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, for a quick description of that dataset, you can check this notebook.
Official Website: http://yann.lecun.com/exdb/mnist/
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.
- TFLearn Quickstart. Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier.
Natural Language Processing
- 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.
tflearn (if using tflearn examples)
For more details about TensorFlow installation, you can check TensorFlow Installation Guide