Deep Learning | By S. Sridhar & D. Narashiman | 1st Edition | Pearson Publication ( English Medium )

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Deep Learning | By S. Sridhar & D. Narashiman | 1st Edition | Pearson Publication ( English Medium ).

Deep Learning is designed as a textbook for undergraduate and postgraduate students, providing a strong foundation in deep learning concepts. The book begins with fundamental topics such as artificial intelligence, machine learning, natural language processing, image processing, and computer vision, which are essential for understanding deep learning technologies. Core deep learning concepts, including neural networks, activation functions, loss functions, optimization, and regularization, are explored in depth. Additionally, the book introduces data fundamentals, ensuring a complete learning experience.

The book covers major deep learning architectures, including Convolutional Neural Networks (CNNs) and Object Detection Networks, with discussions on R-CNN family algorithms, YOLO networks and image segmentation networks. Advanced CNN architectures such as AlexNet, VGGNet, InceptionNet, and ResNet are presented alongside transfer learning applications. The concepts of autoencoders and Recurrent Neural Networks (RNNs), including LSTMs and GRUs, are also introduced. Beyond CNNs, the book also explores Generative AI, covering Large Language Models (LLMs) such as ChatGPT and Generative Adversarial Networks (GANs). It introduces advanced topics like Transformer architectures, along with dedicated chapters on Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), and Deep Reinforcement Learning algorithms.

Features –

Deep learning concepts are presented in a clear, concise, and approachable manner, making complex topics easy to understand.

Hands-on Learning with an online Keras lab manual, enabling practical implementation of deep learning algorithms.

Extensive solved numerical problems, providing clarity and reinforcing deep learning concepts.

Comprehensive learning support, including summaries, glossaries, conceptual questions, numerical problems, and multiple-choice questions.

Engaging pedagogical techniques, such as puzzles and jumbled words, to reinforce key concepts.

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Product Details

SKU / BOOK Code:	Pearson-Deep-Learning-(E)
Publisher:	Pearson Publication
Author:	S. Sridhar & D. Narashiman
Binding Type:	Paperback
No. of Pages:	696
ISBN-10:	9367138660
ISBN-13:	978-9367138663
Edition:	1st Edition
Language:	English Medium
Publish Year:	2025-05
Weight (g):	2000
Product Condition:	New
Reading Age:	Above 10 Years
Country of Origin:	India
Genre:	Engineering Books
Manufacturer:	Pearson Publication
Importer:	Pearson Publication
Packer:	Fullfilled by Supplier

Product Description

Deep Learning | By S. Sridhar & D. Narashiman | 1st Edition | Pearson Publication ( English Medium ).

Deep Learning is designed as a textbook for undergraduate and postgraduate students, providing a strong foundation in deep learning concepts. The book begins with fundamental topics such as artificial intelligence, machine learning, natural language processing, image processing, and computer vision, which are essential for understanding deep learning technologies. Core deep learning concepts, including neural networks, activation functions, loss functions, optimization, and regularization, are explored in depth. Additionally, the book introduces data fundamentals, ensuring a complete learning experience.

The book covers major deep learning architectures, including Convolutional Neural Networks (CNNs) and Object Detection Networks, with discussions on R-CNN family algorithms, YOLO networks and image segmentation networks. Advanced CNN architectures such as AlexNet, VGGNet, InceptionNet, and ResNet are presented alongside transfer learning applications. The concepts of autoencoders and Recurrent Neural Networks (RNNs), including LSTMs and GRUs, are also introduced. Beyond CNNs, the book also explores Generative AI, covering Large Language Models (LLMs) such as ChatGPT and Generative Adversarial Networks (GANs). It introduces advanced topics like Transformer architectures, along with dedicated chapters on Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), and Deep Reinforcement Learning algorithms.

Features –

Deep learning concepts are presented in a clear, concise, and approachable manner, making complex topics easy to understand.

Hands-on Learning with an online Keras lab manual, enabling practical implementation of deep learning algorithms.

Extensive solved numerical problems, providing clarity and reinforcing deep learning concepts.

Comprehensive learning support, including summaries, glossaries, conceptual questions, numerical problems, and multiple-choice questions.

Engaging pedagogical techniques, such as puzzles and jumbled words, to reinforce key concepts.

Search Key - Deep Learning S. Sridhar D. Narashiman, , Deep Learning book by S. Sridhar, , D. Narashiman deep learning textbook, , Pearson deep learning 1st edition, , Deep learning English medium book, , Deep learning S. Sridhar Pearson, , Neural networks book S. Sridhar, , Deep learning fundamentals D. Narashiman, , Deep learning beginner book India, , S. Sridhar AI book, , Deep learning with case studies, , Deep learning concepts Pearson publication, , Introduction to deep learning S. Sridhar, , Deep learning textbook for students, , Deep learning applications book, , Deep learning models S. Sridhar, , Deep learning for computer science, , Artificial intelligence deep learning Pearson, , Deep learning architectures explained, , S. Sridhar neural networks and AI, , Deep learning Indian authors, , Deep learning book for engineering students, , Deep learning 1st edition Pearson, , Deep learning academic reference book, , Deep learning algorithms explained, , Machine learning vs deep learning S. Sridhar, , Deep learning with Python concepts, , AI and deep learning study book, , Deep learning course reference India, , Deep learning theoretical and practical guide, Publisher ‏ : ‎ Pearson Education (28 February 2025); Pearson Education, Language ‏ : ‎ English, Paperback ‏ : ‎ 696 pages, ISBN-10 ‏ : ‎ 9367138660, ISBN-13 ‏ : ‎ 978-9367138663.

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