• Official TensorFlow.js Setup Instructions - includes a script tag for the front end, Node.js install commands, and sample code snippets.
• Official Node.js site - You can download Node.js here.
• Atom Editor - The free text editor used in this course.
• TensorFlow.js Issues page on GitHub - If have any issues running TensorFlow.js on your system, you can find answers or log a bug here.
• WebGL homepage - check if your browser supports WebGL.
• Solutions for Section 2

• TensorFlow.js API docs
• Learning AI if you Suck at Math - Tensors Illustrated
• Solutions for Section 3

• TensorFlow.js API docs
• tsjs-vis - Visualisation library for TensorFlow.js
• tsjs-vis API docs
• Feature scaling - Wikipedia - covers min-max normalisation and alternative techniques.
• Linear equations - Maths is fun
• Solutions for Section 4

• TensorFlow.js API docs
• tsjs-vis API docs
• Solutions for Section 5

• TensorFlow.js API docs
• tsjs-vis API docs
• Training, validation, and test sets - Wikipedia
• Solutions for Section 6

• TensorFlow.js API docs
• tsjs-vis API docs
• Materialize CSS - the CSS framework we used in this section.
• Material Design - Google's Material design system upon which Materialize CSS is based.
• localStorage - MDN Web Docs - information about the localStorage we used in this section to store models.
• Solutions for Section 7

• TensorFlow.js API docs
• tsjs-vis API docs
• Understanding binary cross-entropy / log loss: a visual explanation - In depth mathematical discussion of binary cross entropy with visualisations
• Loss functions for classification - Wikipedia - covers cross entropy and more
• Solutions for Section 8 -

• TensorFlow.js API docs
• tsjs-vis API docs
• Solutions for Section 9

Course support materials

• Solutions for all course sections
• TensorFlow.js posts on the tech.courses blog

General online resources

• Official site: js.tensorflow.org - Getting started, Setup, Tutorials, API reference.
• TensorFlow blog - There's a few posts on TensorFlow.js
• Official site: www.tensorflow.org - Focuses more on Python but may be helpful for learning core Tensorflow concepts.
• Tensorflow playground - Easy way to try out and visualise different neural network configurations.
• Official examples - Examples for both Node.js and browser JS.
• Stackoverflow tag: tensorflow.js - 650 questions at time of writing.
• TensorFlow.js on Reddit - A community for users of TensorFlow.js
• TensorFlow.js Discussion Google Group - An open mailing list to share TensorFlow.js ideas and projects with each other.

TensorFlow.js related JavaScript packages

• tfjs-vis - Visualisation library for TensorFlow.js.
• ML5.js site - This is a high level library built on TensorFlow.js.
• hyperparameters - A library to help learn the optimal hyperparameters for a TensorFlow.js model. At an early stage of development but an exciting premise.

Free video content

• Google Machine Learning Crash Course (with TensorFlow) - In Python, but can adapt exercises for JS.
• The Coding Train - TensorFlow.js color classifier - Build a color classifier with crowd sourced data.
• Applications of Deep Neural Networks course - A University level course on YouTube. In TensorFlow Python but can learn concepts from it.

Books

• Deep Learning in the Browser (2018) - Mainly focuses on TensorFlow.js but covers the theory and other frameworks too.
• Hands-on Machine Learning with JavaScript: Solve complex computational web problems using machine learning - Some coverage of TensorFlow.js
• Deep Learning with JavaScript - This seems to be the first book focused on TensorFlow.js from a well known publisher, and is written by developers of TensorFlow.js

Feedback on this course

If there's anything you expect to learn in this course but didn't, please get in touch. We may be able to add an extra lesson to the course, or at least point you to an appropriate online resource.

If there's content you want to see in a future course, please let us know! We may in future create courses on advanced TensorFlow.js topics and it would be helpful to have your feedback on what you'd like to see covered.