Neural network from scratch
A 784–128–10 feedforward neural network implemented from scratch in NumPy and trained on the MNIST handwritten-digit dataset, reaching 93.8% test accuracy.
I implemented the forward pass, backpropagation, ReLU and sigmoid activations, softmax, cross-entropy loss, and mini-batch stochastic gradient descent without using a machine learning framework.