Deep Learning | By S. Sridhar | 1st Edition 2025 | Pearson Publication ( English Medium )

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Deep Learning | By S. Sridhar | 1st Edition 2025 | Pearson Publication ( English Medium ).

Book Description:

“Deep Learning” by S. Sridhar is a comprehensive and up-to-date textbook designed to introduce the fundamental concepts and advanced techniques of deep learning to students, researchers, and practitioners. Published in 2025, this book covers the latest developments and methodologies in deep learning, emphasizing practical implementation alongside theoretical understanding.

The book delves into neural networks, convolutional networks, recurrent networks, transformers, and emerging architectures. It also covers essential topics such as optimization algorithms, regularization, generative models, and applications in computer vision, natural language processing, and more.

Ideal for learners with a background in machine learning or artificial intelligence, the book balances mathematical rigor with accessible explanations, supported by hands-on examples and Python-based implementations.

Key Features:

Up-to-date Content: Covers the latest advancements in deep learning (2025 edition).

Comprehensive Coverage: Neural networks, CNNs, RNNs, transformers, GANs, and more.

Practical Focus: Includes Python code examples and hands-on projects.

Mathematical Foundations: Detailed explanations of underlying algorithms and theories.

Optimization and Regularization: Techniques to improve model training and generalization.

Real-world Applications: Case studies in computer vision, NLP, speech recognition, etc.

Deep Learning Frameworks: Introduction to popular libraries such as TensorFlow and PyTorch.

Advanced Topics: Attention mechanisms, reinforcement learning integration, explainability.

Clear and Accessible: Written for both beginners and advanced learners.

Exercises and Projects: End-of-chapter exercises and project ideas for practice.

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:	Prsn-Deep-Lernng-(E)
Publisher:	Pearson Publication
Author:	S. Sridhar
Binding Type:	Paperback
No. of Pages:	548
ISBN-10:	9367138660
ISBN-13:	978-9367138663
Edition:	1st Edition
Language:	English Medium
Publish Year:	2025-06
Weight (g):	2000
Product Condition:	New
Reading Age:	Above 10 Years
Country of Origin:	India
Genre:	Textbooks & Study Guides
Manufacturer:	Pearson Publication
Importer:	Pearson Publication
Packer:	Fullfilled by Supplier

Product Description

Deep Learning | By S. Sridhar | 1st Edition 2025 | Pearson Publication ( English Medium ).

Book Description:

“Deep Learning” by S. Sridhar is a comprehensive and up-to-date textbook designed to introduce the fundamental concepts and advanced techniques of deep learning to students, researchers, and practitioners. Published in 2025, this book covers the latest developments and methodologies in deep learning, emphasizing practical implementation alongside theoretical understanding.

The book delves into neural networks, convolutional networks, recurrent networks, transformers, and emerging architectures. It also covers essential topics such as optimization algorithms, regularization, generative models, and applications in computer vision, natural language processing, and more.

Ideal for learners with a background in machine learning or artificial intelligence, the book balances mathematical rigor with accessible explanations, supported by hands-on examples and Python-based implementations.

Key Features:

Up-to-date Content: Covers the latest advancements in deep learning (2025 edition).

Comprehensive Coverage: Neural networks, CNNs, RNNs, transformers, GANs, and more.

Practical Focus: Includes Python code examples and hands-on projects.

Mathematical Foundations: Detailed explanations of underlying algorithms and theories.

Optimization and Regularization: Techniques to improve model training and generalization.

Real-world Applications: Case studies in computer vision, NLP, speech recognition, etc.

Deep Learning Frameworks: Introduction to popular libraries such as TensorFlow and PyTorch.

Advanced Topics: Attention mechanisms, reinforcement learning integration, explainability.

Clear and Accessible: Written for both beginners and advanced learners.

Exercises and Projects: End-of-chapter exercises and project ideas for practice.

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 2025 edition, , Deep learning textbook 2025, , Neural networks deep learning book, , Convolutional neural networks S Sridhar, , Recurrent neural networks deep learning, , Transformers deep learning book, , Generative adversarial networks GANs book, , Deep learning optimization techniques, , Regularization in deep learning, , Python deep learning examples, , TensorFlow deep learning book, , PyTorch deep learning tutorial, , Deep learning applications 2025, , Deep learning fundamentals S Sridhar, , Machine learning vs deep learning book, , Explainable AI deep learning, , Attention mechanisms deep learning, , Deep learning reinforcement learning, , Deep learning project ideas, , Advanced deep learning architectures, , Deep learning algorithms book, , Deep learning model training tips, , Deep learning for computer vision, , Deep learning for NLP applications, , Deep learning book for beginners, , Deep learning exercises and problems, , Latest trends in deep learning 2025, , Deep learning frameworks comparison, , Deep learning with Python book, , Deep learning S Sridhar review.

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