A to Z of Deep Learning and AI

A Broad Map of Deep Learning and Artificial Intelligence

A to Z of Deep Learning and AI begins with a simple premise: the ideas behind intelligent machines can be explained clearly without stripping away their depth. Published by CRC Press in 2025 and edited by Laith Abualigah, this first-edition volume gathers an international group of contributors to survey the field from its foundations to its current research frontiers.

The result is not a narrow tutorial on one library or framework. It is a chapter-by-chapter exploration of how AI and deep learning work, where they are applied, and what questions they raise.

From Neural Networks to Transformers

The opening chapters establish the vocabulary and conceptual foundation. Readers move from an introduction to AI and deep learning into the evolution of machine learning, the structure of neural networks, and the three major learning paradigms: supervised, unsupervised, and reinforcement learning. From there, the book examines convolutional neural networks for image recognition, recurrent neural networks for time-series data, and the increasing importance of data quality.

Later chapters turn to transfer learning and pre-trained models, feedforward architectures, transformers, backpropagation, gradient descent, and regularization techniques for reducing overfitting and underfitting. The sequence gives readers a coherent path through the architectures that define modern deep learning.

Applied Topics and Emerging Frontiers

The second half widens the lens. Dedicated chapters address generative adversarial networks, natural language processing for sentiment analysis and machine translation, deep reinforcement learning, and the scaling of deep learning through distributed systems and cloud computing. The book also explores AI’s intersection with the Internet of Things, quantum computing, and specialized hardware such as GPUs and TPUs.

Applied chapters on optimization algorithms, autonomous robot navigation using double deep Q-networks, and intelligent robotics show how these methods move into physical and operational systems. A closing chapter on future directions considers trends, challenges, and human implications.

Ethics, Scalability, and the Human Dimension

Ethical questions are treated as part of the technical landscape rather than an afterthought. One chapter focuses on ethical frontiers in artificial intelligence and the challenges of machine intelligence. Others consider scalability, hardware limits, and the broader consequences of increasingly capable systems. This balance makes the volume useful for readers who want both conceptual understanding and awareness of the field’s responsibilities.

Who Will Find This Book Useful

The book is aimed at AI enthusiasts, students, and professionals who want a structured survey rather than a single-technique manual. The preface notes that it is intended for those taking their first steps in AI as well as readers seeking to broaden existing expertise. Because it is an edited collection, the chapters can be read in sequence or selected by topic, making it suitable for coursework, self-study, and reference.

• Students looking for a broad introduction to deep learning and AI concepts.
• Practitioners who want to connect familiar methods to adjacent research areas.
• Researchers seeking a concise overview of architectures, applications, and open questions.
• Technical readers curious about how AI intersects with IoT, quantum computing, robotics, and hardware.

Inside the 2025 CRC Press Edition

This first edition includes a preface, a list of contributors, 25 chapters, and an index. The contributing group spans institutions in Jordan, Ghana, South Africa, Malaysia, Pakistan, Australia, Turkey, Egypt, China, Oman, Vietnam, Korea, the UK, Mexico, India, Belgium, the UAE, Iran, Taiwan, and Iraq, among others, giving the volume an international perspective on AI research and practice.

For readers who want a single-volume orientation to deep learning and artificial intelligence, A to Z of Deep Learning and AI offers a clear starting point and a useful reference for the road ahead.