
Course 1: Browser-based Models with TensorFlow.js
You may think that machine learning models can only be trained with supercomputers and big data. This first course shows you how you can train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam.
Week 1: Training and Inference Using Javascript
- Introduction: A conversation with Andrew Ng
- Starting the code
- Training
- First example in code
- Using an imported dataset for a multi-class classifier
- Looking at the data
- One hot encoding
- Designing the neural network
- Iris classifier in code
Week 2: Running Inference on Models in the Browser
- Introduction: A conversation with Andrew Ng
- Creating a convolutional net with JavaScript
- Visualization of the training
- What is a sprite sheet?
- Looking deeper into the code
- Handwriting classifier in code
Week 3: Running Models in a Browser
- Introduction: A conversation with Andrew Ng
- Text-based models
- Toxicity classifier in code
- MobileNet
- Using a pre-trained MobileNet
- Converting to JavaScript
Week 4: Rock Paper Scissors
- Introduction: A conversation with Andrew Ng
- Transfer learning
- Building a simple web page
- Retraining the MobileNet model
- Training function
- Code to capturing the data
- The dataset class
- Training the network
- Rock paper scissors in code