Principles and Labs for Deep Learning (eBook)

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. - Introduces readers to the usefulness of neural networks and Deep Learning methods - Provides readers with in-depth understanding of the architecture and operation of Deep Convolutional Neural Networks - Demonstrates the visualization needed for designing neural networks - Provides readers with an in-depth understanding of regression problems, binary classification problems, multi-category classification problems, Variational Auto-Encoder, Generative Adversarial Network, and Object detection

Dr. Shih-Chia Huang is a Full Professor with the Department of Electronic Engineering, National TaipeiUniversity of Technology, Taiwan, and an International Adjunct Professor with the Faculty of Business andInformation Technology, University of Ontario Institute of Technology, Oshawa, ON, Canada. He is currentlythe Chapter Chair of the IEEE Taipei Section Broadcast Technology Society, an Associate Editor of the IEEESensors 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, ArtificialIntelligence, image processing, video coding, intelligent video surveillance systems, cloud computing, big dataanalytics, and mobile applications and systems.

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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. - Introduces readers to the usefulness of neural networks and Deep Learning methods- Provides readers with in-depth understanding of the architecture and operation of Deep Convolutional Neural Networks- Demonstrates the visualization needed for designing neural networks- Provides readers with an in-depth understanding of regression problems, binary classification problems, multi-category classification problems, Variational Auto-Encoder, Generative Adversarial Network, and Object detection