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.
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.
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.
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.
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