• 
• Install
• Get Started
• Programmer's Guide
• Tutorials
• Performance

• Getting Started
• Getting Started with Eager Execution
• Estimators
• For Beginners
• Premade Estimators
• Details
• Checkpoints
• Feature Columns
• Datasets Quick Start
• Creating Custom Estimators

• Get Started

Get Started

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.

TensorFlow provides many APIs. This section focuses on the high-level APIs. If you are new to TensorFlow, begin by reading one of the following documents:

• Get Started with Eager Execution is for machine learning beginners and uses Eager Execution.
• Get Started with Graph Execution is also for machine learning beginners and uses Graphs and Sessions.
• Get Started with Estimators assumes some machine learning background and uses an Estimator.

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:

• The Low Level Introduction demonstrates how to use TensorFlow outside of the Estimator framework, for debugging and experimentation.
• The Programmer's Guide details major TensorFlow components.
• The Tutorials provide walkthroughs of a variety of TensorFlow models.