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Internet of Things for Architects

You're reading from   Internet of Things for Architects Architecting IoT solutions by implementing sensors, communication infrastructure, edge computing, analytics, and security

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788470599
Length 524 pages
Edition 1st 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 (15) Chapters Close

Preface 1. The IoT Story FREE CHAPTER 2. IoT Architecture and Core IoT Modules 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. Routers and Gateways 9. IoT Edge to Cloud Protocols 10. Cloud and Fog Topologies 11. Data Analytics and Machine Learning in the Cloud and in the Fog 12. IoT Security 13. Consortiums and Communities 14. Other Books You May Enjoy

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 in the chapter.

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, support vector machines, fuzzy logic, and so on.

Evolutionary computation such as...

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