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COMPLETE HANDBOOK OF DEEP LEARNING: CNNs, RNNs & GENERATIVE MODELS

By Sundaresan K

COMPLETE HANDBOOK OF DEEP LEARNING: CNNs, RNNs & GENERATIVE MODELS

By Sundaresan K

0 - Default Title
Description
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.
Product details
Binding:
Paperback
Number of Pages:
196
Release Date:
2025-11-20
Publication Date:
2025-11-20
Publisher:
LAP LAMBERT Academic Publishing
Languages:
Original: English
ISBN10:
6209273858
ISBN13:
9786209273858
GPSR Manufacturer Reference:
[email protected]
Weight:
310 g
Height:
150 cm
Width:
220 cm
Thickness:
12 cm
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