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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
The reader starts by learning the basics of AI and IoT devices and how to read IoT data from various sources and streams. Then we introduce various ways to implement AI with examples in TensorFlow, scikit learn, and Keras. The topics covered include machine learning, deep learning, genetic algorithms, reinforcement learning, and generative adversarial networks. We also show the reader how to implement AI using distributed technologies and on the cloud. Once the reader is familiar with AI techniques, then we introduce various techniques for different kinds of data generated and consumed by IoT devices, such as time series, images, audio, video, text, and speech. After explaining various AI techniques on various kinds of IoT data, finally, we share some case studies with the reader from the four major categories of IoT solutions: personal IoT, home IoT, industrial IoT, and smart city IoT.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
The reader starts by learning the basics of AI and IoT devices and how to read IoT data from various sources and streams. Then we introduce various ways to implement AI with examples in TensorFlow, scikit learn, and Keras. The topics covered include machine learning, deep learning, genetic algorithms, reinforcement learning, and generative adversarial networks. We also show the reader how to implement AI using distributed technologies and on the cloud. Once the reader is familiar with AI techniques, then we introduce various techniques for different kinds of data generated and consumed by IoT devices, such as time series, images, audio, video, text, and speech. After explaining various AI techniques on various kinds of IoT data, finally, we share some case studies with the reader from the four major categories of IoT solutions: personal IoT, home IoT, industrial IoT, and smart city IoT.