A set of Jupyter notebooks implementing simple neural networks described in Michael Nielsen's book.

Just some notebooks I wrote to research some fun stuff in hobby time

This notebook goes through how to build a neural network using only numpy. The network classifies tumours, identifying if they are malignant or benign. This notebook uses the Breast Cancer Wisconsin dataset.

This notebook serves as a step-by-step tutorial on building a neural network from scratch using only NumPy and Pandas, focusing on the MNIST dataset.

This notebook demonstrates a neural network implementation using NumPy, without TensorFlow or PyTorch. Trained on the MNIST dataset, it features an architecture with input layer (784 neurons), two hidden layers (132 and 40 neurons), and an output layer (10 neurons) with sigmoid activation.