FEM: Machine Learning with JavaScript

Course: https://frontendmasters.com/workshops/machine-learning/

https://fem-ml-workshop.netlify.app/

https://github.com/charliegerard/fem-ml-workshop

https://charliegerard.dev/projects

Basics

https://github.com/tensorflow/tfjs-models

Overfitting: model is too close to training data, value of 1. Can't predict.

• Supervised learning: given labelled data.
• Unsupervised learning: given a dataset without labels. Use is clustering.
• Semi-supervised learning: combines supervised and unsupervised. Can achieve higher accuracy.
• Reinforcement learning: training provides rewards and punishment. Used in gaming.

Pre-trained Models

Finding pre-trained models

• Kaggle and more specifically TensorFlow.js models
• TensorFlow.js models
• Hugging face

Choosing a Model

• What are you building?
• Quality of the training data
• Quantity of the training data
• Can you have access to the list of classes/labels?

Tools

• TensorFlow.js
• ml5.js
• Brain.js
• ConvNetJS
• Keras.js

Transfer Learning

Reusing pre-trained model for a new task.

Quick start

• Teachable machine

https://teachablemachine.withgoogle.com/train/image

https://experiments.withgoogle.com/tiny-motion-trainer

Building a Model

https://cs231n.github.io/convolutional-networks/

Split training and test data.

https://neurosity.co/tech-specs

https://www.youtube.com/playlist?list=PLOU2XLYxmsILr3HQpqjLAUkIPa5EaZiui

https://www.linkedin.com/pulse/welcome-tensorflowjs-monthly-newsletter-jason-mayes/

https://experiments.withgoogle.com/tiny-motion-trainer

https://neurotechx.com/