Foundations of Deep Learning

Part I

Deep learning begins with a deceptively simple idea: layered computation can learn useful representations directly from data.

This Part establishes the conceptual and mathematical vocabulary used throughout the book. Chapter 1 positions deep learning within artificial intelligence and machine learning, traces its development, and introduces representation learning. Chapter 2 opens the neural network itself, connecting artificial neurons, layers, activation functions, and major architecture families. Chapter 3 then explains how those networks learn through loss functions, gradient descent, backpropagation, regularization, and disciplined training practice.

Together, these chapters move from what deep learning is to how networks are structured and finally to how their parameters become useful. The resulting foundation prepares the reader to examine specialized architectures without treating them as disconnected inventions.