Rust

A deep dive into building a Retrieval-Augmented Generation system in Rust, from understanding the fundamentals to measuring results.

Establishing baselines and exploring learning rate sensitivity - the first steps in neural network experimentation.

Testing the neural network implementation training on logic gates, visualising decision boundaries, and understanding what the network actually learned.

Learning how neural networks work by implementing one from scratch. Part 1 covers the core building blocks: matrices, activations, and backpropagation.