Deep Learning from Scratch: Building with Python from First Principles

A comprehensive understanding of neural networks by building them from first principles using Python and NumPy. The author argues that mastering deep learning requires multiple mental models, specifically representing concepts through mathematical equations, visual diagrams, and executable code. The text begins with foundational building blocks, such as functions and derivatives, before explaining how the chain rule allows for the calculation of gradients in nested functions. These concepts are essential for understanding computational graphs, which serve as the structural basis for modern AI models. By implementing these elements from the ground up, the book prepares readers to eventually use high-level frameworks like PyTorch with a deep intuition for their internal mechanics.

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