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Specialization: Basics of TensorFlow for JavaScript development

Before starting on the learning materials below, you should:

  1. Be comfortable with browser programming using HTML & JavaScript

  2. Be familiar with using the command line to run node.js scripts

This curriculum is for people who want to:

  1. Build ML models in JavaScript

  2. Run existing TensorFlow.js models

  3. Deploy ML models to web browsers

TensorFlow.js lets you develop ML models in JavaScript, and use ML directly in the browser or on Node.js. To learn more about TensorFlow.js, and what can be done with it, check out this talk at Google I/O.

Step 1: Quick introduction to machine learning in the browser.

To get a quick introduction on basics for ML in JavaScript, watch this video series on YouTube, which takes you from first principles, to building a neural network to do basic classification.

Introductory online courses
Getting started with TensorFlow.js by TensorFlow

A 3-part series that explores both training and executing machine learned models with TensorFlow.js, and shows you how to create a machine learning model in JavaScript that executes directly in the browser.

Step 2: Dive deeper into Deep Learning

To get a deeper understanding of how neural networks work, and a broader understanding of how to apply them to different problems, the book Deep Learning with JavaScript is a great place to start. It is accompanied by a large number of examples from GitHub so you can practice working with machine learning in JavaScript.

This book will demonstrate how to use a wide variety of neural network architectures, such as Convolutional Neural Networks, Recurrent Neural Networks, and advanced training paradigms such as reinforcement learning. It also provides clear explanations of what is actually happening with the neural network in the training process.

Introductory online courses
Deep Learning with JavaScript by Shanqing Cai, Stanley Bileschi, Eric D. Nielsen with Francois Chollet

Written by the main authors of the TensorFlow library, this book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.

Step 3: Practice with examples using TensorFlow.js

Practice makes perfect, and getting hands on experience is the best way to lock in the concepts. With your knowledge of neural networks, you can more easily explore the open sourced examples created by the TensorFlow team. They are all available on GitHub, so you can delve into the code and see how they work. To experiment with common use cases, you can start exploring convolutional neural networks using the mnist example, try transfer learning using the mnist-transfer-cnn example, or see how recurrent neural networks are structured with the addition-rnn example.

Examples built with TensorFlow.js

A repository on GitHub that contains a set of examples implemented in TensorFlow.js. Each example directory is standalone so the directory can be copied to another project.

Explore our tutorials to learn how to get started with TensorFlow.js

The TensorFlow tutorials are written as Jupyter notebooks and run directly in Google Colab—a hosted notebook environment that requires no setup. Click the Run in Google Colab button.

Step 4: Make something new!

Once you've tested your knowledge, and practiced with some of the TensorFlow.js examples, you should be ready to start developing your own projects. Take a look at our pretrained models, and start building an app. Or you can train your own model using data you've collected, or by using public datasets. Kaggle and Google Dataset Search are great places to find open datasets for training your model.