Description
Neural Networks and Deep Learning is a practical and approachable introduction to how modern neural networks work, written with a strong focus on intuition rather than heavy theory. The book walks readers through the core ideas behind deep learning — including perceptrons, backpropagation, gradient descent, overfitting, convolutional networks, and recurrent networks — while gradually building real implementations from scratch in Python. Instead of treating AI as a black box, it explains why neural networks learn, how training actually happens, and what design choices improve performance, making it especially valuable for programmers and self-learners who want to understand the foundations behind today’s AI systems without needing an advanced mathematics background.
