Machine Learning in JavaScript with TensorFlow.js

Machine Learning in JavaScript with TensorFlow.js
Machine Learning in JavaScript with TensorFlow.js
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7 Hours | 2.46 GB

Master machine learning with JavaScript and TensorFlowJS. Add artificial intelligence to websites, Node.js and web apps!

Updated for 2020!

Interested in using Machine Learning in JavaScript applications and websites? Then this course is for you!

This is the tutorial you’ve been looking for to become a modern JavaScript machine learning master in 2020. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master – join the TensorFlow.js revolution.

This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes.

Throughout the course we use house price data to ask ever more complicated questions; “can you predict the value of this house?”, “can you tell me if this house has a waterfront?”, “can you classify it as having 1, 2 or 3+ bedrooms?”. Each example builds on the one before it, to reinforce learning in easy and steady steps.

Machine Learning in TensorFlow.js provides you with all the benefits of TensorFlow, but without the need for Python. This is demonstrated using web based examples, stunning visualisations and custom website components.

This course is fun and engaging, with Machine Learning learning outcomes provided in bitesize topics:

Part 1 – Introduction to TensorFlow.js
Part 2 – Installing and running TensorFlow.js
Part 3 – TensorFlow.js Core Concepts
Part 4 – Data Preparation with TensorFlow.js
Part 5 – Defining a model
Part 6 – Training and Testing in TensorFlow.js
Part 7 – TensorFlow.js Prediction
Part 8 – Binary Classification
Part 9 – Multi-class Classification
Part 10 – Conclusion & Next Steps

As a bonus, for every student, we provide you with JavaScript and HTML code templates that you can download and use on your own projects.

What you’ll learn

  • Machine Learning in Javascript and TensorFlowJS
  • Deep Learning and Neural Network concepts
  • Why TensorFlow for JavaScript is a game changer
  • Defining machine learning models
  • How to install and run TensorFlowJS
  • How TensorFlowJS is optimised
  • Training machine learning models
  • Data preparation for machine learning
  • How to make accurate predictions
  • Linear regression
  • Binary classification
  • Multi-class classification
  • Heatmap visualisation
  • Scatter-plot visualisation
  • Importing and normalising data
  • How to manage memory in TensorFlowJS
  • Tensor mathematics
  • Saving machine learning models
  • Inputting and outputting using a web browser
  • Javascript and machine learning integration
  • Shuffling, and splitting data
  • In-depth labs for practical development
Table of Contents

Introduction
1 Introduction What is TensorFlow.js
2 Course Overview
3 Machine Learning Concepts
4 Overview of Artificial Neural Networks
5 Lab TensorFlow Playground
6 Summary

Installing and running TensorFlow.js
7 TensorFlow.js environments
8 Running TensorFlow.js in the browser
9 WebGL optimisations in TensorFlow.js
10 Running TensorFlow.js on Node.js
11 Review
12 Lab Install and run TensorFlow.js in the browser
13 Lab Install and run TensorFlow.js on Node.js
14 Summary

TensorFlow.js Core Concepts
15 TensorFlow.js APIs
16 What is a Tensor
17 Tensor Math Operations Ops API
18 Memory Management in TensorFlow.js
19 Review
20 Lab Tensor Math and Memory Management
21 Summary

Data Preparation with TensorFlow.js
22 Linear Regression
23 Reading data from CSV
24 Visualising the data
25 Preparing Features and Labels
26 Normalisation with TensorFlow.js
27 Splitting into Training and Testing data
28 Review
29 Lab Prepare the Data
30 Summary

Defining a model
31 Introduction to Layers API
32 Creating Layers in TensorFlow.js
33 Inspecting a TensorFlow.js model
34 Compiling the model
35 Review
36 Lab Creating a Model
37 Summary

Training and Testing in TensorFlow.js
38 Introduction to Training and Testing
39 Training with model.fit
40 Visualising loss with tfjs-vis
41 Testing with model.evaluate
42 Training and testing review lab
43 Lab TensorFlow.js Training and Testing
44 Summary

TensorFlow.js Prediction
45 Integrating TensorFlow.js with a UI
46 Saving and loading a model
47 Making Predictions
48 Visualising Predictions
49 Non-linear Regression
50 Prediction review labs
51 Lab TensorFlow.js predictions
52 Lab Beyond Linear Regression
53 Lab (optional) Training without Layers API
54 Summary

Binary Classification
55 Introduction Binary Classification
56 Visualising Classification Data
57 Preparing Multiple Features
58 Binary Classification Model
59 Visualising Classification with Heatmaps
60 Binary Classification Predictions
61 Binary Classification Review Lab
62 Lab TensorFlow.js Binary Classification
63 Summary

Multi-class Classification
64 Introduction Multi-class Classification
65 One hot encoding
66 Multi-class classification model
67 Visualising Multi-class Predictions
68 Multi-class prediction
69 Multi-class Classification Review Lab
70 Lab TensorFlow.js Multi-class Classification
71 Summary

Conclusion Next Steps
72 Course Review
73 Next steps with TensorFlow.js
74 Resources for going deeper with TensorFlow.js