As we mentioned earlier, we are using transfer learning that does not require training from scratch; retraining of the models with a new dataset will sufficiently work in many cases. We retrained two popular architectures or models of CNN, namely Incentive V3 and Mobilenet V1, on a desktop computer, which is replicating the city council’s server. In both models, it took less than an hour to retrain the models, which is an advantage of the transfer learning approach. We need to understand the list of key arguments before running the retrain.pyfile, which is in the code folder. If we type in our Terminal (in Linux or macOS) or Command Prompt (Windows) python retrain.py -h, we shall see a window like the following screenshot with additional information (that is, an overview of each argument). The compulsory argument is the image directory, and it is one of...
Hands-On Deep Learning for IoT
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Hands-On Deep Learning for IoT
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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.
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Hands-On Deep Learning for IoT
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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