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IoT and Edge Computing for Architects

You're reading from   IoT and Edge Computing for Architects Implementing edge and IoT systems from sensors to clouds with communication systems, analytics, and security

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781839214806
Length 632 pages
Edition 2nd Edition
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Author (1):
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Perry Lea Perry Lea
Author Profile Icon Perry Lea
Perry Lea
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Table of Contents (17) Chapters Close

Preface 1. IoT and Edge Computing Definition and Use Cases 2. IoT Architecture and Core IoT Modules FREE CHAPTER 3. Sensors, Endpoints, and Power Systems 4. Communications and Information Theory 5. Non-IP Based WPAN 6. IP-Based WPAN and WLAN 7. Long-Range Communication Systems and Protocols (WAN) 8. Edge Computing 9. Edge Routing and Networking 10. Edge to Cloud Protocols 11. Cloud and Fog Topologies 12. Data Analytics and Machine Learning in the Cloud and Edge 13. IoT and Edge Security 14. Consortiums and Communities 15. Other Books You May Enjoy
16. Index

Machine learning in IoT

Machine learning is not a new computer science development. On the contrary, mathematical models for data fitting and probability go back to the early 1800s, and Bayes' theorem and the least squares method of fitting data. Both are still widely used in machine learning models today, and we will briefly explore them later in the chapter.

A brief history of AI and machine learning milestones

It wasn't until Marvin Minsky (MIT) produced the first neural network devices called perceptrons in the early 1950s that computing machines and learning were unified. He later wrote a paper in 1969 that was interpreted as a critique of the limitations of neural networks. Certainly, during that period, computational horsepower was at a premium. The mathematics were beyond the reasonable resources of IBM S/360 and CDC computers. As we will see, the 1960s introduced much of the mathematics and foundations of artificial intelligence in areas such as neural nets...

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