Building neural networks from scratch provides foundational understanding of deep learning. While popular frameworks like TensorFlow and PyTorch simplify neural net implementation, they may obscure core concepts. This guide explains foundational neural network components using Python and NumPy to solve tasks like the XOR logic gate. It addresses mathematical foundations, forward and backward propagation, and outlines the steps to train a simple neural network without deep learning frameworks.
Table of contents
PrerequisitesIntroductionUnderstanding the concept of neuronsConstruction of neural networks from scratchComparison of builds using deep learning frameworksConclusionSort: