Deep Learning Techniques and Applications

Deep Learning techniques have been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics, and chemistry. This book, “Principles and Labs for Deep Learning,” provides the knowledge and techniques needed to help readers design and develop deep learning models.

Understanding Neural Networks and Deep Learning

Neural networks and Deep Learning methods 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.

Practical Applications and Experiments

Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome. This book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning.

Key Topics Covered

The book covers key topics such as:

• Introduction to the usefulness of neural networks and Deep Learning methods
• Architecture and operation of Deep Convolutional Neural Networks
• Visualization needed for designing neural networks
• Regression problems, binary classification problems, multi-category classification problems
• Understanding Variational Auto-Encoder, Generative Adversarial Network, and Object detection

Author and Publication Details

Author: Shih-Chia Huang

Publication Date: July 6, 2021

Language: English

ISBN: 9780323901987, 9780323901994

Published by: Elsevier Science