Pollyanna Grows Up

A Daughter of the Land

Her Father's Daughter

Laddie

The Life of George Washington: All Five Volumes in a Single File

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 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. What you will learn ?Get acquainted with different neural network architectures and their suitability in IoT ?Understand how deep learning can improve the predictive power in your IoT solutions ?Capture and process streaming data for predictive maintenance ?Select optimal frameworks for image recognition and indoor localization ?Analyze voice data for speech recognition in IoT applications ?Develop deep learning-based IoT solutions for healthcare ?Enhance security in your IoT solutions ?Visualize analyzed data to uncover insights and perform accurate predictions Who this book is for If you’re an IoT developer, data scientist, or deep learning enthusiast who wants to apply deep learning techniques to build smart IoT applications, this book is for you. Familiarity with machine learning, a basic understanding of the IoT concepts, and some experience in Python programming will help you get the most out of this book. Table of Contents 1.End-to-End Life Cycle of IoT 2.Deep Learning Architectures for IoT 3.Image Recognition in IoT 4.Audio/Speech/Voice Recognition in IoT 5.Indoor localization in IoT 6.Physiological and Psychological State Detection in IoT 7.Security and privacy for IoT 8.Predictive Maintenance for IoT 9.Deep learning in Healthcare IoT 10.What’s next: Wrapping Up and Future Directions

Diary and Letters of Madame d'Arblay

The Magic Egg and Other Stories

Narrative of the Life of Frederick Douglass

At the Foot of the Rainbow

The Illuminati: The World's Most Dangerous Secret Society

Commentary on the Laws of England. Book the First

Molten Heart: A Fire Drake Love Story

Psychological Methods That Could Help You to Change Your Life!

Jonathan Wild

The Adventures of Sir Launcelot Greaves

Chakras & intelligences multiples

The Adventures of Roderick Random

Buying Real Estate

Positive Thinking Is the Key to Mental Health

The Monk, A Romance