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Deep learning: (Record no. 10075)

MARC details
000 -LEADER
fixed length control field	04690nam a2200217 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20250718224331.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	250718b \|\|\|\|\|\|\|\| \|\|\|\| 00\| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9781119861867
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	006.31
Item number	MAR
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Martinez-Ramon, Manel
245 ## - TITLE STATEMENT
Title	Deep learning:
Remainder of title	a practical introduction
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc.	John Wiley & Sons Ltd
Place of publication, distribution, etc.	Hoboken
Date of publication, distribution, etc.	2024
300 ## - PHYSICAL DESCRIPTION
Extent	xxiii, 392 p.
365 ## - TRADE PRICE
Price type code	USD
Price amount	110.00
500 ## - GENERAL NOTE
General note	Table of contents:<br/>About the Authors xv<br/><br/>Foreword xvii<br/><br/>Preface xix<br/><br/>Acknowledgment xxi<br/><br/>About the Companion Website xxiii<br/><br/>1 The Multilayer Perceptron 1<br/><br/>1.1 Introduction 1<br/><br/>1.2 The Concept of Neuron 2<br/><br/>1.3 Structure of a Neural Network 14<br/><br/>1.4 Activations 21<br/><br/>1.5 Training a Multilayer Perceptron 22<br/><br/>1.6 Conclusion 37<br/><br/>2 Training Practicalities 41<br/><br/>2.1 Introduction 41<br/><br/>2.2 Generalization and Overfitting 42<br/><br/>2.3 Regularization Techniques 45<br/><br/>2.4 Normalization Techniques 50<br/><br/>2.5 Optimizers 52<br/><br/>2.6 Conclusion 58<br/><br/>3 Deep Learning Tools 61<br/><br/>3.1 Python: An Overview 61<br/><br/>3.2 NumPy 72<br/><br/>3.3 Matplotlib 83<br/><br/>3.4 Scipy 97<br/><br/>3.5 Scikit-Learn 107<br/><br/>3.6 Pandas 116<br/><br/>3.7 Seaborn 125<br/><br/>3.8 Python Libraries for NLP 131<br/><br/>3.9 TensorFlow 138<br/><br/>3.10 Keras 141<br/><br/>3.11 Pytorch 144<br/><br/>3.12 Conclusion 149<br/><br/>4 Convolutional Neural Networks 153<br/><br/>4.1 Introduction 153<br/><br/>4.2 Elements of a Convolutional Neural Network 153<br/><br/>4.3 Training a CNN 160<br/><br/>4.4 Extensions of the CNN 166<br/><br/>4.5 Conclusion 184<br/><br/>5 Recurrent Neural Networks 187<br/><br/>5.1 Introduction 187<br/><br/>5.2 RNN Architecture 188<br/><br/>5.3 Training an RNN 191<br/><br/>5.4 Long-Term Dependencies: Vanishing and Exploding Gradients 199<br/><br/>5.5 Deep RNN 201<br/><br/>5.6 Bidirectional RNN 203<br/><br/>5.7 Long Short-Term Memory Networks 204<br/><br/>5.8 Gated Recurrent Units 218<br/><br/>5.9 Conclusion 221<br/><br/>6 Attention Networks and Transformers 225<br/><br/>6.1 Introduction 225<br/><br/>6.2 Attention Mechanisms 227<br/><br/>6.3 Transformers 242<br/><br/>6.4 BERT 249<br/><br/>6.5 GPT-2 256<br/><br/>6.6.1 Comparison between ViTs and CNNs 264<br/><br/>6.7 Conclusion 269<br/><br/>7 Deep Unsupervised Learning I 273<br/><br/>7.1 Introduction 273<br/><br/>7.2 Restricted Boltzmann Machines 274<br/><br/>7.3 Deep Belief Networks 278<br/><br/>7.4 Autoencoders 279<br/><br/>7.5 Undercomplete Autoencoder 284<br/><br/>7.6 Sparse Autoencoder 285<br/><br/>7.7 Denoising Autoencoders 287<br/><br/>7.8 Convolutional Autoencoder 288<br/><br/>7.9 Variational Autoencoders 291<br/><br/>7.10 Conclusion 297<br/><br/>8 Deep Unsupervised Learning II 301<br/><br/>8.1 Introduction 301<br/><br/>8.2 Elements of GAN 303<br/><br/>8.3 Training a GAN 305<br/><br/>8.4 Wasserstein GAN 309<br/><br/>8.5 DCGAN 312<br/><br/>8.6 cGAN 316<br/><br/>8.7 CycleGAN 318<br/><br/>8.8 StyleGAN 323<br/><br/>8.9 StackGAN 328<br/><br/>8.10 Diffusion Models 333<br/><br/>8.11 Conclusion 338<br/><br/>9 Deep Bayesian Networks 341<br/><br/>9.1 Introduction 341<br/><br/>9.2 Bayesian Models 342<br/><br/>9.3 Bayesian Inference Methods for Deep Learning 344<br/><br/>9.4 Conclusion 352<br/><br/>Problems 353<br/><br/>List of Acronyms 355<br/><br/>Notation 359<br/><br/>Bibliography 365<br/><br/>Index 387<br/><br/>[https://www.wiley.com/en-us/Deep+Learning%3A+A+Practical+Introduction-p-9781119861867#tableofcontents-section]
520 ## - SUMMARY, ETC.
Summary, etc.	An engaging and accessible introduction to deep learning perfect for students and professionals<br/><br/>In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples.<br/><br/>Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find:<br/><br/>Thorough introductions to deep learning and deep learning tools<br/>Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures<br/>Practical discussions of recurrent neural networks and non-supervised approaches to deep learning<br/>Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks<br/>Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.<br/><br/>(https://www.wiley.com/en-us/Deep+Learning%3A+A+Practical+Introduction-p-9781119861867)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Deep Learning
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name	Ajith, Meenu
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name	Rajendra Kurup, Aswathy
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	Book
Source of classification or shelving scheme	Dewey Decimal Classification

Holdings
Withdrawn status	Lost status	Source of classification or shelving scheme	Damaged status	Not for loan	Collection code	Bill No	Bill Date	Home library	Current library	Shelving location	Date acquired	Source of acquisition	Cost, normal purchase price	Total Checkouts	Full call number	Accession Number	Date last seen	Copy number	Cost, replacement price	Price effective from	Koha item type
		Dewey Decimal Classification			IT & Decisions Sciences	TB909	09-07-2025	Indian Institute of Management LRC	Indian Institute of Management LRC	General Stacks	07/16/2025	Technical Bureau India Pvt. Ltd.	6334.90		006.31 MAR	008870	07/16/2025	1	9746.00	07/16/2025	Book