Deep Neural Network Applications
- Length: 148 pages
- Edition: 1
- Language: English
- Publisher: CRC Pr I Llc
- Publication Date: 2022-04-28
- ISBN-10: 0367211467
- ISBN-13: 9780367211462
- Sales Rank: #0 (See Top 100 Books)
The world is on the verge of fully ushering in the fourth industrial revolution, of which artificial intelligence (AI) is the most important new general-purpose technology. Like the steam engine that led to the widespread commercial use of driving machineries in the industries during the first industrial revolution; the internal combustion engine that gave rise to cars, trucks, and airplanes; electricity that caused the second industrial revolution through the discovery of direct and alternating current; and the Internet, which led to the emergence of the information age, AI is a transformational technology. It will cause a paradigm shift in the way’s problems are solved in every aspect of our lives, and, from it, innovative technologies will emerge. AI is the theory and development of machines that can imitate human intelligence in tasks such as visual perception, speech recognition, decision-making, and human language translation.
This book provides a complete overview on the deep learning applications and deep neural network architectures. It also gives an overview on most advanced future-looking fundamental research in deep learning application in artificial intelligence. Research overview includes reasoning approaches, problem solving, knowledge representation, planning, learning, natural language processing, perception, motion and manipulation, social intelligence and creativity. It will allow the reader to gain a deep and broad knowledge of the latest engineering technologies of AI and Deep Learning and is an excellent resource for academic research and industry applications.
Cover Page Title Page Copyright Page Dedication Foreword Preface Table of Contents 1. Introduction 1.1 Artificial and Deep Neural Networks 1.2 Evolution: Where are We Now? 1.3 Motivation 2. Deep Learning Basics 2.1 Applied Math 2.1.1 Forward Propagation 2.1.2 Backward Propagation 2.1.3 Optimization of DNNs 3. Neural Network Structures 3.1 Feed Forward Neural Network 3.2 Convolutional Neural Networks (CNN) 3.3 Deconvolutional Neural Network (DNN) 3.4 Deep Convolutional Inverse Graphics Network (DCIGN) 3.5 Recurrent Neural Network (RNN) 3.6 Kohonen Network (KN) 3.7 Deep Residual Network (DRN) 3.8 Long Short-Term Memory (LSTM) 3.9 Gated Recurrent Neural Networks (GRU) 3.10 Bidirectional Recurrent Neural Network (BRNN) 3.11 Hopfield Network (HN) 3.12 Generative Adversarial Networks (GAN) 3.13 Deep Belief Network (DBN) 4. Top Applications of Deep Learning Across Industries 4.1 Agriculture 4.2 Banking and Finance 4.3 Education 4.4 Healthcare 4.5 Legal and Politics 4.6 Military and Security 4.7 Service and Marketing 4.8 Social Media and Entertainment 4.9 Transportation 4.10 Other Applications 5. Discussions and Criticism 5.1 Challenges We Face Today 5.2 Future of Deep Neural Networks References Index
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