Normalization Techniques In Deep Learning
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Normalization Techniques in Deep Learning
This book presents and surveys normalization techniques with a deep analysis in training deep neural networks. In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures. The author provides guidelines for elaborating, understanding, and applying normalization methods. This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.
Mastering Deep Learning: From Basics to Advanced Techniques
Author: Dr.M.Kasthuri
language: en
Publisher: SK Research Group of Companies
Release Date: 2024-07-10
Dr.M.Kasthuri, Associate Professor, Department of Computer Science, Bishop Heber College, Tiruchirappalli, Tamil Nadu, India. Mrs.K.Kalaiselvi, Guest Lecturer, Department of Computer Science, Thanthai Periyar Government Arts and Science College, Tiruchirappalli, Tamil Nadu, India.
Deep Learning
Welcome to "Deep Learning: A Comprehensive Guide," a book meticulously designed to cater to the needs of learners at various stages of their journey into the fascinating world of deep learning. Whether you are a beginner embarking on your first exploration into artificial intelligence or a seasoned professional looking to deepen your expertise, this book aims to be your trusted companion. Deep learning, a subset of machine learning, has revolutionized the field of artificial intelligence, enabling advancements that were once thought to be the stuff of science fiction. From autonomous vehicles to sophisticated natural language processing systems, deep learning has become the backbone of many cutting-edge technologies. Understanding and mastering deep learning is not just a desirable skill but a necessity for anyone looking to thrive in the modern tech landscape. What This Book Offers This book is not just a theoretical exposition but a practical guide designed to provide you with a holistic learning experience. Here's a glimpse of what you can expect: Structured Content: Starts with neural network basics and advances to topics like convolutional, recurrent, and generative adversarial networks. Each chapter builds on the previous, ensuring a comprehensive learning journey. Online Practice Questions: Each chapter includes practice questions from basic to advanced levels to test and reinforce your understanding. Videos: Instructional videos complement the book's content, offering step-by-step explanations and real-life applications. Exercises and Projects: Includes exercises and hands-on projects that simulate real-world problems, providing practical experience. Lab Activities: Features lab activities using frameworks like TensorFlow and PyTorch for hands-on experimentation with deep learning models. Case Studies: Illustrates the application of deep learning in industries such as healthcare, finance, and entertainment, highlighting its transformative potential. Comprehensive Coverage: Covers a broad spectrum of topics, from theoretical foundations to practical implementations, latest advancements, ethical considerations, and future trends. Who Should Use This Book? This book is designed for: Students and Academics: Pursuing studies in computer science, data science, or related fields. Industry Professionals: Enhancing skills or transitioning into roles involving deep learning. Embarking on the journey to master deep learning is both challenging and rewarding. This book is designed to make that journey as smooth and enlightening as possible. We hope that the combination of theoretical knowledge, practical exercises, projects, and real-world applications will equip you with the skills and confidence needed to excel in the field of deep learning.