English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 75 lectures (7h 16m) | 1.52 GB
Master machine learning with JavaScript and TensorFlowJS. Add artificial intelligence to websites, Node.js and web apps!
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 2022. 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 3
- Deep Learning and Neural Network concepts
- Why TensorFlow for JavaScript is a game changer
- Defining machine learning models
- How to install and run TensorFlowJS 3
- How TensorFlowJS 3 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 3
- 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 New TensorFlow.js for React Native
12 Review
13 Lab Install and run TensorFlow.js in the browser
14 Lab Install and run TensorFlow.js on Node.js
15 Summary
TensorFlow.js Core Concepts
16 TensorFlow.js APIs
17 What is a Tensor
18 Tensor Math Operations & Ops API
19 Memory Management in TensorFlow.js
20 Review
21 Lab Tensor Math and Memory Management
22 Summary
Data Preparation with TensorFlow.js
23 Linear Regression
24 Reading data from CSV
25 Visualising the data
26 Preparing Features and Labels
27 Normalisation with TensorFlow.js
28 Splitting into Training and Testing data
29 Review
30 Lab Prepare the Data
31 Summary
Defining a model
32 Introduction to Layers API
33 Creating Layers in TensorFlow.js
34 Inspecting a TensorFlow.js model
35 Compiling the model
36 Review
37 Lab Creating a Model
38 Summary
Training and Testing in TensorFlow.js
39 Introduction to Training and Testing
40 Training with model.fit
41 Visualising loss with tfjs-vis
42 Testing with model.evaluate
43 Training and testing review & lab
44 Lab TensorFlow.js Training and Testing
45 Summary
TensorFlow.js Prediction
46 Integrating TensorFlow.js with a UI
47 Saving and loading a model
48 Making Predictions
49 Visualising Predictions
50 Non-linear Regression
51 Prediction review & labs
52 Lab TensorFlow.js predictions
53 Lab Beyond Linear Regression
54 Lab (optional) Training without Layers API
55 Summary
Binary Classification
56 Introduction Binary Classification
57 Visualising Classification Data
58 Preparing Multiple Features
59 Binary Classification Model
60 Visualising Classification with Heatmaps
61 Binary Classification Predictions
62 Binary Classification Review & Lab
63 Lab TensorFlow.js Binary Classification
64 Summary
Multi-class Classification
65 Introduction Multi-class Classification
66 One hot encoding
67 Multi-class classification model
68 Visualising Multi-class Predictions
69 Multi-class prediction
70 Multi-class Classification Review & Lab
71 Lab TensorFlow.js Multi-class Classification
72 Summary
Conclusion & Next Steps
73 Course Review
74 Next steps with TensorFlow.js
75 Resources for going deeper with TensorFlow.js
Resolve the captcha to access the links!