If you are new to machine learning, we recommend taking the following online course prior to diving into TensorFlow documentation:
- Machine Learning Crash Course, which introduces machine learning concepts and encourages experimentation with existing TensorFlow code.
TensorFlow is a tool for machine learning. While it contains a wide range of functionality, TensorFlow is mainly designed for deep neural network models.
The easiest way to get started with TensorFlow is using Eager Execution.
- Get Started with Eager Execution, is for anyone new to machine learning or TensorFlow.
TensorFlow provides many APIs. The remainder of this section focuses on the Estimator API which provide scalable, high-performance models. To get started with Estimators begin by reading one of the following documents:
- Get Started with Graph Execution, which is aimed at readers new to machine learning.
- Premade Estimators, which is aimed at readers who have experience in machine learning.
Then, read the following documents, which demonstrate the key features in the high-level APIs:
- Checkpoints, which explains how to save training progress and resume where you left off.
- Feature Columns, which shows how an Estimator can handle a variety of input data types without changes to the model.
- Datasets Quick Start, which introduces TensorFlow's input pipelines.
- Creating Custom Estimators, which demonstrates how to build and train models you design yourself.
For more advanced users: