This book will provide you with a thorough overview of a class of advanced machine
learning techniques called deep learning (DL), to facilitate the analytics and learning in
various IoT applications. A hands-on overview will take you through what each process is,
from data collection, analysis, modeling, and a model's performance evaluation, to various
IoT application and deployment settings.
You’ll learn how to train convolutional neural networks (CNN) for developing
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).
This book is intended for anyone who wants to use DL techniques to analyze and
understand IoT generated big and real-time data streams with the power of TensorFlow,
Keras, and Chainer. If you want to build your own extensive IoT applications that work,
and that can predict smart decisions in the future, then this book is what you need! Hence,
this book is dedicated to IoT application developers, data analysts, or DL enthusiasts who
do not have much background in complex numerical computations, but who want to know
what DL actually is.