Hands-on deep learning for IoT: train neural network models to develop intelligent IoT applications

✍ Scribed by Karim, Md. Rezaul; Razzaque, Mohammad Abdur

Publisher: Packt Publishing
Year: 2019
Tongue: English
Category: Library

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✦ Synopsis

Implement popular deep learning techniques to make your IoT applications smarter

Key Features

Understand how deep learning facilitates fast and accurate analytics in IoT

Build intelligent voice and speech recognition apps in TensorFlow and Chainer

Analyze IoT data for making automated decisions and efficient predictions

Book Description

Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale.

Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and...

✦ Table of Contents

IoT ecosystems, deep learning techniques and frameworks. The end-to-end life cycle of the IoT --
Deep learning architectures for IoT --
Hands-on deep learning application development for IoT. --
Image recognition in IoT --
Audio/speech/voice recognition in IoT --
Indoor localization in IoT --
Physiological and psychological state detection in IoT --
IoT security --
Advanced aspects and analytics in IoT. Predictive maintenance for IoT --
Deep learning in healthcare IoT --
What's next - wrapping up and future directions --
References.

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