Inhaltsangabe
This book offers a comprehensive and structured introduction to the foundations, architectures, and applications of deep learning. Beginning with core mathematical concepts such as linear algebra, probability, and optimization, it builds a strong base for understanding modern neural networks. The text explores key ideas like model capacity, bias-variance trade-off, overfitting, and hyperparameter tuning. Readers are then guided through major deep learning architectures, including Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) and LSTMs for sequence modeling, and advanced generative models like Autoencoders, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs). Each chapter presents clear explanations, diagrams, and practical examples to simplify complex concepts. Designed for students, educators, and AI practitioners, the book provides both theoretical depth and practical insights. It serves as a complete reference for anyone seeking to understand, build, and apply deep learning models effectively across real-world problems in computer vision, natural language processing, and generative AI.
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Über die Autorin bzw. den Autor
Mr. Sundaresan K is working as an Assistant Professor, and Dr. Nallakumar R is working as an Associate Professor in the Department of Artificial Intelligence and Data Science, Karpagam Institute of Technology. Their areas of interest are Neural Networks and Data Analytics.
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