Dive into the world of neural networks with this comprehensive JavaScript course using Brain.js. Learn to build and implement neural networks in browsers and Node.js through practical, hands-on coding exercises. Explore key concepts such as propagation, network structure, and layers, then apply your knowledge to real-world examples including stock price prediction, sentiment detection, and reinforcement learning. Master working with objects, normalization techniques, and recurrent neural networks. By the end, you'll be equipped to solve a variety of problems using neural networks, all explained in an accessible manner by Brain.js lead developer Robert Plummer. Perfect for JavaScript developers looking to expand their skills into the exciting field of artificial intelligence and machine learning.
Overview
Syllabus
) Course introduction.
) Our first neural net!.
) How they learn - Propagation.
) How they learn - Structure.
) How they learn - Layers.
) Working with objects!.
) Learning more than numbers.
) Example: Counter.
) Normalization.
) Example: Stock price predictor.
) Predicting multiple steps.
) Example: A recurrent neural network that learns math.
) Example: Number detector.
) Example: Writing a children's book.
) Example: Sentiment detection.
) RNN inputs and outputs.
) Example: Simple reinforcement learning.
) Example: Recommendation engine.
) Closing thoughts.
Taught by
freeCodeCamp.org
