Principles and Labs for Deep Learning

Erscheinungsjahr 2021
ISBN: 978-0-323-90198-7
Verlag: William Andrew Publishing

Buch, Englisch, 366 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 1000 g

Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of results. The structured methods and labs provided by Dr. Huang and Dr. Le enable readers to become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub.  Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome.

Deep Learning has been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics and chemistry. Because of the huge scope of knowledge in Deep Learning, a lot of time is required to understand and deploy useful, working applications, hence the importance of this new resource. Both theory lessons and experiments are included in each chapter to introduce the techniques and provide source code examples to practice using them. All Labs for this book are placed on GitHub to facilitate the download. The book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning.

Autoren/Hrsg.

Huang, Shih-Chia

Le, Trung-Hieu

Fachgebiete

• Sozialwissenschaften Psychologie Allgemeine Psychologie Kognitionspsychologie Lernen
• Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik E-Learning, Bildungstechnologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein E-Learning
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik

Inhaltsverzeichnis

1. Introduction to TensorFlow2.02. Regression Problem3. Binary classification problem4. Multi-category Classification Problem5. Training Neural Network6. Advanced TensorFlow2.07. Advanced TensorBoard8. Convolutional Neural Network Architectures9. Transfer Learning10. Variational Auto-Encoder11. WGAN-GP12. Object Detection

Über Autor(innen)

Huang, Shih-Chia
Dr. Shih-Chia Huang is a Full Professor with the Department of Electronic Engineering, National Taipei University of Technology, Taiwan, and an International Adjunct Professor with the Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, ON, Canada. He is currently the Chapter Chair of the IEEE Taipei Section Broadcast Technology Society, an Associate Editor of the IEEE Sensors Journal and Electronic Commerce Research and Applications, respectively. He has authored and coauthored more than 100 journal and conference papers and holds more than 60 patents in the U.S., Europe, Taiwan, and China. His research interests include intelligent multimedia systems, Deep Learning, Artificial Intelligence, image processing, video coding, intelligent video surveillance systems, cloud computing, big data analytics, and mobile applications and systems.

Le, Trung-Hieu
Dr. Trung-Hieu Le is an Assistant Professor with the Department of Electronic Engineering, National Taipei University of Technology, Taiwan, and a Lecturer in the Faculty of Information Technology, Hung Yen University of Technology and Education, Vietnam. His research interests include deep learning, image processing, object detection, and object recognition.