This book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With deep learning emerging as a vibrant field of research and development in numerous industrial applications, there is a pressing need for accessible resources that provide comprehensive examples and quick guidance. Unfortunately, many existing books on the market tend to emphasize theoretical aspects, leaving newcomers scrambling for practical guidance. This book takes a different approach by focusing on practicality while keeping theoretical concepts to a necessary minimum. The book begins by laying a foundation of basic information on deep learning, gradually delving into the subject matter to explain and illustrate the limitations of existing algorithms. A dedicated chapter is allocated to evaluating the performance of multiple algorithms on specific datasets, highlighting techniques and strategies that can address real-world challenges when deep learning is employed. By consolidating all necessary information into a single resource, readers can bypass the hassle of scouring scattered online sources, gaining a one-stop solution to dive into deep learning for object detection and classification. To facilitate understanding, the book employs a rich array of illustrations, figures, tables, and code snippets. Comprehensive code examples are provided, empowering readers to grasp concepts quickly and develop practical solutions. The book covers essential methods and tools, ensuring a complete and comprehensive coverage that enables professionals to implement deep learning algorithms swiftly and effectively.

This book is designed to equip professionals with the necessary skills to thrive in the active field of deep learning, where it has the potential to revolutionize traditional problem-solving approaches. This book serves as a practical companion, enabling readers to grasp concepts swiftly and embark on building practical solutions.

Offers a special chapter devoted to performance evaluation of deep learning algorithms Demonstrates illustrative colorful block diagrams, figures, and full code examples to clearly present ideas involved Gives contents geared for both professionals and newcomers to deep learning

Autorentext

Dr. Jonah Gamba received a B.Sc. degree in Electrical and Electronics Engineering from the University of Zimbabwe in 1994 and an M.Sc. in Computer Science and Engineering from Zhejiang University, Hangzhou, China, in 2000. He obtained a Ph.D. degree in Mathematical Information Systems from Saitama University in 2005. He was a postdoctoral research fellow in information theory at Tsukuba University from 2006 to 2008. He has been actively involved in research and development activities after obtaining his Ph.D. He conducted research in automotive applications of millimeter-wave radar for advanced vehicle safety and comfort systems. At Tsukuba University's Tsukuba Advanced Research Alliance, he conducted research in video and image processing algorithms as a JST CREST Project postdoctoral researcher. Dr. Gamba has authored and co-authored several publications in signal processing and related fields. As an inventor, he has registered multiple patents in signal processing applications. His current interests are signal processing for automotive applications, functional safety management, and remote sensing. He has been a visiting researcher of Seikei University and Tokyo University. He is a member of IEEE Signal Processing Society.

Inhalt

Chapter 1. Basic Approaches in Object Detection and Classification by Deep Learning.- Chapter 2. Requirements for Hands-on Approach to Deep Learning.- Chapter 3. Building Deep Learning Models.- Chapter 4. The Building Blocks of Machine Learning and Deep Learning.- Chapter 5. Remote Sensing Example for Deep Learning.