Google: Google AI for JavaScript developers with TensorFlow.js

• Common terms and what they mean
• How Machine Learning works (without formal mathematical definitions)
• Overview of the TensorFlow.js library
• Advantages of using ML in JavaScript
• Ways to consume or create Machine Learning models
• How to use pre-made “off the shelf” models
• What Tensors are in Machine Learning
• How to use Tensors with ML models
• How to write a simple custom model
• Perceptrons (artificial neuron) and how they work
• Linear regression to predict numbers using single neuron
• Multi layered perceptrons for handling more complex data
• How to use models that use Convolutional Neural Networks for images
• How to convert Python models to JavaScript
• Transfer learning - reusing existing trained models with your own data
• Inspiring projects others are creating to seed your own future ideas

1. Welcome to TensorFlow.js

1.1 Course overview and how people are applying skills you will learn

1.2 Who is the course aimed at

1.3 Introduce yourself

1.4 What will you be making?

1.5 Your background

2. Introduction to ML & TensorFlow.js

2.1 What's the difference - AI / ML / Deep Learning

2.2 Demystifying Machine Learning

2.3 Test your knowledge

2.4 What is TensorFlow.js

2.5 Three ways to use ML

2.6 Test your knowledge

3. Using Pre-Made models

3.1 What are pre-made models?

3.2 Selecting the right model to use

3.3 Quiz

3.4 Case study - using a pre-made model for object detection

3.5 Make your own smart security camera

3.6 Loading saved TensorFlow.js models directly

3.7 Tensors in Tensors out

3.8 Coding practice

4. Writing custom models

4.1 Rolling your own models

4.2 Starting simple: Predicting a number

4.3 Going deeper: Perceptrons

4.4 Using a perceptron to predict numbers

4.5 Implementing a perceptron for linear regression

4.6 Test your knowledge

4.7 Going deeper: Multi-layered perceptron

4.8 Implementing a multi-layered perceptron

4.9 Training a multi layered perceptron for classification task

4.10 Test your knowledge

4.11 Beyond perceptrons

4.12 Researching other ML architecture types

5. Transfer Learning

5.1 Reusing existing models

5.2 Make Teachable Machine yourself

5.3 Project ideas

5.4 Test your knowledge

6. Reusing models from Python

6.1 Benefits of model reuse

6.2 Converting Python saved models6.10 Test knowledge

6.3 Convert a model yourself

6.4 Comment Spam detection

6.5 Choosing an appropriate model

6.6 Use converted model to check spam in real application

6.7 Dealing with edge cases

6.8 Retraining a model to deal with custom edge cases

6.9 What models would you convert?

7. To the future and beyond

7.1 Machine Learning as a Web Engineer

7.2 Find a friend to continue hacking

7.3 Course summary and how to join the TensorFlow.js community