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  1. Introduction to Machine Learning with TensorFlow.js
  2. Introduction
  3. Introduction

Introduction

Let's get physical

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With TensorFlow.js, JavaScript and Machine Learning are finally joining forces. Companies like Uber and Airbnb are already using TensorFlow.js in production. I haven’t been this energized by new technology in years.

You can already build some fantastic web applications, take a look at these:

  • The emoji scavenger hunt[1], a web-based game where you have to take pictures of real-life items matching an emoji.

  • The emotion detector app[2], train an ML model to detect the emotion shown in a face.

  • You can even train an in-browser self-driving car using the metacar project[3].

Look, Machine Learning is hard. I’m not going to pretend it isn’t. However, I plan to make it easy to learn by:

  • Teaching you everything you need, all the maths, from scratch (no previous knowledge required).

  • Teaching you just the essentials, focus only on the things that will be useful for you as a JavaScript engineer.

  • Teaching by doing, we’re going to build four different apps from scratch, each getting progressively more complex but each teaching you something important.

Agenda

  1. Introduction

    We will cover an overview of Machine Learning and Neural Networks as well as a history of TensorFlow and TensorFlow.js.

  2. Project 1: Using a pre-trained model

    In this first project you will learn how to use a pre-trained model and build your first AI-powered application.

  3. TensorFlow

    Now we will dig deeper into TensorFlow itself we’ll cover what Tensors are, how to create them with TensorFlow.js and how to perform basic mathematical calculations using Tensors.

  4. Optimization

    In this section we will explain the core function of TensorFlow and what makes up the field of Machine Learning, Optimization. We will learn what a loss function is and how to use TensorFlow.js to optimise some values based on the loss function.

  5. Project 2: Linear & Polynomial regression

    In this lecture we will cover what regression is and when would you use it, why we start with regression and how to build your first regression model.

  6. Project 3: Recognizing handwritten numbers

    In this section we will start using the layers API from TensorFlow.js and build a much more sophisticated application that recognizes handwritten digits. We will then use the same problem and solve it using a variety of different ML algorithms.

  7. Project 4: Transfer Learning

    Finally we bring all that knowledge together into Transfer Learning, we will take a pre-trained model and train a new model on top of it. Transfer Learning is one of the fastest and least computationally intensive ways to make use of Machine Learning in JavaScript.

How to get the best out of this course?

Each application has been selected very carefully; I layer information on carefully and slowly, so you learn TensorFlow.js at a reasonable pace. That’s why I recommend you go through this course in order, complete each project before moving onto the next.

Besides, you will only learn if you do, don’t imagine you can skim through the material just reading the source code. As I said, Machine Learning is hard; there are no short cuts just well-made training material and a willingness to learn.


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