Educational Implementation · Jupyter Notebook
A lightweight deep learning framework implemented from scratch using NumPy/CuPy. supports customizable architectures, forward and back propagation, dropout, He/Glorot init, and mini-batch training. Designed for flexibility,it provides a foundation for building neural networks while giving insights into the inner workings of deep learning models
DIYNeuralNet is a from-scratch NumPy-based neural network library designed for educational purposes, featuring hand-derived forward and backward passes for layers, activations, losses, and optimizers, accompanied by detailed documentation and runnable notebooks.
Merit describes the code at this commit. Later pushes count once the owner rescans. How merit is scored
How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.
Read from the project's code at its scored commit.
The owner keeps this project's limitations private.
