Best Resources to Learn TensorFlow

Google’s TensorFlow is currently the most famous deep learning library in the world. TensorFlow offers a comprehensive, flexible ecosystem of tools, libraries and community resources.

It allows developers to push the state-of-the-art in machine learning (ML), build and deploy ML powered applications.

TensorFlow architecture works in three parts:

  • Preprocessing the data
  • Build the model
  • Train and estimate the model

Table of Contents

Tutorials and Courses


  • TensorBoard – TensorBoard provides the visualization and tooling needed for machine learning experimentation.
  • Colaboratory – This tool allows you to write and execute Python in your browser, with zero configuration required.
  • What-If Tool – Using WIT, you can test performance in hypothetical situations, analyze the importance of different data features, and visualize model behavior across multiple models and subsets of input data, and for different ML fairness metrics.
  • Tensor Playground – A playground with a neural network on your browser.
  • MLPerf – Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.

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