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About Edge Intelligence: Deep learning-enabled edge computing
Edge Intelligence: Deep Learning-enabled edge computing is a book that targets researchers and practitioners who are interested in applying intelligence without compromising data privacy. The book reveals the existing edge-AI techniques and forecasts future edge-AI integration methods. The book delves into edge computing architectures after describing relevant basic technologies such as IoT, cloud computing, and other security-related architectures. The book starts with an explanation of all relevant basic technologies. It offers a smooth transition from the basics to insightful practical sessions for practitioners. The ideas of providing innovative ideas and applications in the later part of the book can enthuse researchers and developers to engage themselves in innovating newer products with the application of Edge Intelligence. Part of IOP Series in Next Generation Computing. Edge intelligence is deployed in two broad ways: (i) machine learning-based intelligence; and (ii) deep learning-based intelligence. Deep Learning-based edge intelligence: Deep Learning, in general, is a sub-field of Machine Learning that mimics the learning processes of humans. Our human brains learn different data based on several histories of information. This involves computationally powerful computers or computing devices to learn a large volume of data that arises from data-intensive applications, such as IoT-enabled applications. In recent years, the majority of real-world applications have included Deep Learning algorithms on edge nodes, considering the performance efficiency and learning accuracy with respect to the input regional data. The most widely applied Deep Learning algorithms are convolutional neural networks (CNNs), image segmentation algorithms, generative adversarial networks (GANs), reinforcement learning (RL), and transformers. The application of Deep Learning algorithms on edge-level nodes increases privacy and avoids latency while considering a large chunk of data in an automated fashion.
Detailed Information
| Author: | Shajulin Benedict |
|---|---|
| Publication Year: | 2024 |
| ISBN: | 9780750355919 |
| Language: | English |
| File Size: | 9 |
| Format: | |
| Price: | FREE |
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