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Deep Learning | 1st Edition | - Pearson


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  • ISBN13:9789367138663
  • ISBN10:9367138663
  • Age:14+
  • Language:English
  • Author:S. Sridhar, D. Narashiman
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Highlights

  • ISBN13:9789367138663
  • ISBN10:9367138663
  • Age:14+
  • Language:English
  • Author:S. Sridhar, D. Narashiman
  • Publisher:Pearson Education
  • Pages:696
  • Binding:Paperback
  • Year:2025
  • Edition:1
  • Edition Details:Latest
  • BIS/ISI License number:NA
  • BIS/ISI required:NA
  • SUPC: SDL096774531

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Country of Origin or Manufacture or Assembly India
Common or Generic Name of the commodity Language Learning
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Description

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 –

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

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

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

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

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

About the Author -

Dr S. Sridhar is presently a Professor at the Department of Information Science and Technology, College of Engineering, Guindy Campus, Anna University, Chennai. He has an active teaching and research experience for more than 30 years. His academic career includes 25 years of teaching and research at Anna University, around four years of teaching and research at the National Institute of Technology (NIT), Tiruchirappalli, and around two years of teaching at SRM University.

He is an approved PhD guide for Anna University. He has published many technical papers in reputed International and Indian Journals. Also, he has conducted, participated in, and served as a resource person for several short-term courses, seminars, and workshops conducted at the national and International level. He has delivered lectures on Design and analysis of algorithms in the EDUSAT television Programme and E-Pathasala for Anna University. He has also served project internship in the Council of Scientific and Industrial Research (CSIR), Chennai, and the Indian Space Research Organization (ISRO), Bengaluru.

He has authored books on Digital Image Processing, Design and Analysis of Algorithms, Machine Learning, and Python Programming.

Dr D. Narashiman is presently a faculty in the Department of Information Science and Technology, College of Engineering, Guindy Campus, Anna University, Chennai. His PhD research work is based on translating Text to Sign Language gestures. He has won the best project and best paper award based on his research work. His areas of Interest include Deep Learning, Natural language Processing, Animation, and Video Analytics.



Book Contents –

1. Introduction to Deep Learning 2. Introduction to Artificial Neural Networks 3. Introduction to Activation and Loss Functions 4. Introduction to Optimization 5. Introduction to Regularization 6. Understanding Data 7. Introduction to Regression and Classification 8. Introduction to Computer Vision and Image Processing 9. Convolutional Neural Networks 10. Transfer Learning 11. Introduction to Object Detection 12. Recurrent Neural Networks 13. Introduction To Autoencoders 14. Natural Language Processing for Deep Learning 15. Transformer Architecture and Large Language Models 16. Generative AI and Generative Adversarial Networks 17. Boltzmann Machines and Deep Belief Networks 18. Deep Reinforcement Learning

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