Data preprocessing is an essential step for a deep learning pipeline. The HAR and FER2013 datasets are preprocessed well. However, the downloaded image files for the second dataset of use case two are not preprocessed. As shown in the preceding image, the images are not uniform in size or pixels and the dataset is not large in size; hence, they require data augmentation. Popular augmentation techniques are flip, rotation, scale, crop, translation, and Gaussian noise. Many tools are available for each of these activities. You can use the tools or write their own script to do the data augmentation. A useful tool is Augmentor, a Python library for machine learning. We can install the tool in our Python and use it for augmentation. The following code (data_augmentation.py) is a simple data augmentation process that executes flipping, rotation, cropping, and resizing...
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Table Of Contents
Hands-On Deep Learning for IoT
By :
Hands-On Deep Learning for IoT
By:
Overview of this book
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 Chainer for IoT.
You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN).
You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced.
By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making.
Table of Contents (15 chapters)
Preface
Free Chapter
Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
The End-to-End Life Cycle of the IoT
Deep Learning Architectures for IoT
Section 2: 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
Section 3: Advanced Aspects and Analytics in IoT
Predictive Maintenance for IoT
Deep Learning in Healthcare IoT
What's Next - Wrapping Up and Future Directions
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