A to Z of Deep Learning and AI (eBook, PDF)

A Journey into Deep Learning and AI
Redaktion: Abualigah, Laith

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 216
• Erscheinungstermin: 9. September 2025
• Englisch
• ISBN-13: 9781040451069
• Artikelnr.: 74870897

Autorenporträt

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.

Inhaltsangabe

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.