Shih-ChiaHuang,Trung-HieuLe
Principles and Labs for Deep Learning
Principles and Labs for Deep Learning
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Excellent- More about Principles and Labs for Deep Learning
Principles and Labs for Deep Learning is a comprehensive guide that teaches readers how to design and develop deep learning models using TensorFlow. It includes theory lessons, illustrated examples, and step-by-step labs to help readers become proficient in building CNNs with custom APIs, Keras APIs, Keras Applications, and TensorFlow Hub. The book covers a wide range of applications in deep learning, including computer vision, audio processing, robotics, natural language processing, bioinformatics, and chemistry.
Format: Paperback / softback
Length: 366 pages
Publication date: 25 June 2021
Publisher: Elsevier Science & Technology
Principles and Labs for Deep Learning is a comprehensive guide that provides readers with the knowledge and techniques required to design and develop deep learning models. The book offers a theoretical foundation for deep learning techniques, accompanied by comprehensive illustrations and explanations through TensorFlow source code examples. Additionally, the visualization of results aids in analyzing the effectiveness of the implemented models.
Structured methods and labs, developed by Dr. Huang and Dr. Le, enable readers to gain proficiency in TensorFlow and build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub. Each chapter includes a corresponding Lab with step-by-step instructions to facilitate practical learning outcomes.
Deep Learning has successfully been applied in a wide range of fields, including computer vision, audio processing, robotics, natural language processing, bioinformatics, and chemistry. Due to the vast scope of knowledge in Deep Learning, understanding and deploying practical applications can be time-consuming. Hence, the importance of this new resource.
The book incorporates both theory lessons and experiments to introduce the techniques and provide source code examples for readers to practice. All Labs for the book are available on GitHub for easy download. It is assumed that readers have a basic understanding of Python for programming and basic Machine Learning prior to reading this book.
Weight: 1008g
Dimension: 217 x 275 x 30 (mm)
ISBN-13: 9780323901987
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