This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation.
It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional ‘raw’ TensorFlow implementations, you can also find the latest TensorFlow API practices (such as layers, estimator, dataset, …).
Update (27.08.17): TensorFlow v1.3 is recommended. Added many new examples (kmeans, random forest, multi-gpu training, layers api, estimator api, dataset api …).
If you are using older TensorFlow version (0.11 and under), please have a look here.
0 – Prerequisite
1 – Introduction
2 – Basic Models
3 – Neural Networks (Supervised, Unsupervised)
4 – Utilities
5 – Data Management
6 – Multi GPU