Deep Learning in the Visual Domain

Deep Learning in the Visual Domain: Backpropagation in Theory, Code and Practice for Convolutional Networks and Visual Transformers

Master backpropagation from mathematical first principles to Python implementation—and apply it to convolutional networks, Vision Transformers, and autonomous driving.

Deep Learning in the Visual Domain provides a practical and mathematically rigorous approach to understanding how modern deep learning models actually learn. Rather than relying entirely on high-level frameworks that hide the underlying mechanics, this book takes readers behind the abstractions and demonstrates how neural networks perform forward and backward propagation, calculate gradients, and update their weights.

Written by experienced AI researchers, the book bridges the gap between deep learning theory and working Python code. Each major concept is developed from first principles and accompanied by implementations that allow readers to see exactly how information and gradients flow through a neural network.

The journey begins with the fundamentals of image filters and neural networks before progressing to Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs). Dedicated chapters explain and derive the forward and backward passes for individual layer types, including the mathematics behind the attention mechanism, and connect these derivations directly to Python implementations.

The book culminates in a practical autonomous driving application. Using the e2e_driver simulation environment, readers can design, train, and evaluate end-to-end autonomous driving models capable of navigating vehicles through a synthetic cityscape. This provides an opportunity to apply the concepts of computer vision, neural networks, CNNs, and Vision Transformers to a realistic AI problem in a controlled virtual environment.

What You Will Learn

• Understand backpropagation and gradient computation from mathematical first principles
• Implement deep learning components and neural network layers directly in Python
• Explore the fundamentals of image filters and computer vision
• Understand the architecture and learning process of Convolutional Neural Networks (CNNs)
• Learn how Vision Transformers (ViTs) process visual information
• Derive and implement forward and backward passes for important neural network layers
• Understand the mathematics and implementation of the attention mechanism
• Trace gradient flow and weight updates without relying entirely on framework abstractions
• Build and experiment with CNN and Vision Transformer architectures
• Apply deep learning techniques to autonomous driving
• Use the e2e_driver simulation environment to train and evaluate driving models in a virtual cityscape

Who This Book Is For

This book is designed for deep learning practitioners, AI and machine learning professionals, graduate students, and senior undergraduate students who want to understand what happens inside modern neural networks rather than simply calling pre-built APIs.

It is particularly valuable for readers who want to strengthen their understanding of backpropagation, computer vision, CNNs, Vision Transformers, neural network mathematics, and deep learning with Python.

By combining mathematical derivations, transparent Python implementations, modern computer vision architectures, and a hands-on autonomous driving project, Deep Learning in the Visual Domain provides a comprehensive foundation for understanding and building deep learning systems from the ground up.