Neural Networks: Exploring the Basics and Building from Scratch

This document is an introductory guide to neural networks covering their architecture (input, hidden, output layers), the neuron model and activation functions, example Python implementations for a simple network and training loop, and explanations of backpropagation, gradient descent, hyperparameters, and debugging techniques, with references for further learning.