"In "A to Z of deep learning and AI" readers are invited to embark on an exhilarating exploration of the cutting-edge world of artificial intelligence (AI) and deep learning. This book is your essential guide to demystifying the complex yet fascinating universe of intelligent machines. The advent of deep learning has ignited a revolution in the field of AI. The book begins by unraveling the fundamental concepts that underpin deep learning, allowing even those without prior technical knowledge to grasp the intricacies of neural networks and machine learning algorithms. It offers a comprehensive roadmap to understanding the key principles, from the simplest perceptrons to the most advanced convolutional and recurrent networks, explaining how they can perceive, learn, and make intelligent decisions. Readers will be captivated by the real-world applications of deep learning and AI, showcasing how these technologies have transformed industries such as healthcare, finance, and self-driving cars. Case studies and expert insights provide valuable perspectives on the enormous potential and ethical challenges in the field. What sets "A to Z of deep learning and AI" apart is its focus on accessibility. The book bridges the gap between theoretical concepts and practical implementation. It empowers readers to embark on their own AI journeys, with step-by-step guidance on building and training neural networks, working with popular frameworks, and handling big data. As the AI and deep learning landscape evolves rapidly, this book keeps pace. It delves into emerging trends such as generative adversarial networks (GANs), reinforcement learning, and the ethical considerations surrounding AI development. "A to Z of deep learning and AI" is an indispensable companion for AIenthusiasts, students, and professionals alike. It provides the knowledge and tools to harness the potential of intelligent machines and contribute to the ongoing AI revolution"-- Provided by publisher.
The book unravels fundamental concepts that underpin deep learning, allowing even those without prior technical knowledge to grasp the intricacies of neural networks and machine learning algorithms. It provides roadmap to understanding the key principles, from the simplest perceptron to the most advanced convolutional and recurrent networks, explaining how they can perceive, learn, and make intelligent decisions. Real-world applications of deep learning and AI are given, showcasing how these technologies have transformed industries such as healthcare, finance, and self-driving cars. Case studies and expert insights provide valuable perspectives on the enormous potential and ethical challenges in the field. The book bridges the gap between theoretical concepts and practical implementation. It empowers readers to embark on their own AI journeys, with step-by-step guidance on building and training neural networks, working with popular frameworks, and handling big data. As the AI and deep learning landscape evolves rapidly, this book keeps pace. It delves into emerging trends such as generative adversarial networks (GANs), reinforcement learning, and the ethical considerations surrounding AI development. An essential reading for AI enthusiasts, students, and professionals alike. It provides the knowledge and tools to harness the potential of intelligent machines and contribute to the ongoing AI revolution.
This book demystifies deep learning and AI, making complex concepts accessible to all readers. It blends theory with practical guidance, covering neural networks, real-world applications, and ethical considerations. With insights on GANs and reinforcement learning, it empowers to build intelligent systems and drive innovation.
Preface. Introduction to Artificial Intelligence and Deep Learning. The
Evolution of Machine Learning: From Traditional Algorithms to Deep Learning
Paradigms. Unpacking Neural Networks: The Brains Behind Deep Learning.
Supervised Learning: Teaching Machines with Labeled Data. Unsupervised
Learning: Discovering Patterns without Labels: Health Care, E-Commerce, and
Cybersecurity. Reinforcement Learning: Machines that Learn by Doing.
Convolutional Neural Networks: The Power Behind Image Recognition. Recurrent
Neural Networks and its Applications in Time Series Data. Understanding the
Role of Data in Deep Learning. The Impact of Transfer Learning and
Pre-trained Models on Model Performance. From Feedforward to Transformers: An
In-Depth Exploration of Deep Learning Architectures. Backpropagation and
Gradient Descent: Key Techniques for Neural Network Optimization. Mitigating
Overfitting and Underfitting in Deep Learning: A Comprehensive Study of
Regularization Techniques. Ethical Frontiers in Artificial Intelligence:
Addressing the Challenges of Machine Intelligence. Generative Adversarial
Networks (GANs): A Paradigm Shift and Revolutionizing Content Creation with
Artificial Intelligence Creativity. Sentiment Analysis and Machine
Translation-based NLP for Human Language and Machine Understanding. Deep
Reinforcement Learning: Bridging Learning and Control in Intelligent Systems.
Optimizing Deep Learning Scalability: Harnessing Distributed Systems and
Cloud Computing for Next-Generation AI. The Intersection of AI and the
Internet of Things (IoT): Transforming Data into Intelligence. Quantum
Computing with Artificial Intelligence: A Paradigm Shift in Intelligent
Systems. Future Computational Power of AI Hardware: A Comparative Analysis of
GPUs and TPUs. Reinforcement Learning-based Optimization Algorithms: A
Survey. Autonomous Robot Navigation System Based on Double Deep Q-Network.
Intelligent Robotics using Optimization Algorithms: A Survey. Future
Directions in Artificial Intelligence: Trends, Challenges, and Human
Implications.
Laith Abualigah is the Director of the Department of International Relations and Affairs and an Associate Professor at the Computer Science Department at Al Al-Bayt University, Jordan. He received a PhD from the School of Computer Science at Universiti Sains Malaysia, Malaysia, in 2018. According to the report published by Clarivate, he is one of the Highly Cited Researchers for 2021-2024 and the 1% Influential Researcher by the Web of Science. He is also 2% top scientists in the world (Stanford University). He has published more than 650 journal papers and books, which collectively have been cited more than 27000 times (H-index = 73). His main research interests are Artificial Intelligence, Meta-heuristic Modeling, and Optimization Algorithms, Evolutionary Computations, Information Retrieval, Text clustering, Feature Selection, Combinatorial Problems, Optimization, Advanced Machine Learning, Big data, and Natural Language Processing. He currently serves as an associate editor of many prestigious journals.
