Neural networks, from scratch.
A learning lab for understanding neural networks through Python, NumPy, manual backpropagation, and optimizer experiments.
What I worked on
Practice implementing and debugging the mathematical building blocks of a multilayer perceptron, alongside formal neural-network coursework.
How the pieces fit
- Work through forward passes, loss functions, and gradients.
- Practice manual backpropagation and compare optimizer behavior.
- Connect coursework to small, inspectable implementations.
Where it stands
Independent exercises currently live in my learning notes. The public repository is reserved for future publication and currently contains a license.
What I’m taking forward
Writing the smaller pieces myself helps me evaluate the larger systems I build with AI assistance.
See the work for yourself
The linked source and documentation give the most useful context for this project’s implementation and scope.
Explore the documentation