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​Hands-On Artificial Intelligence for IoT

​Hands-On Artificial Intelligence for IoT - Second Edition

By : Dr. Amita Kapoor
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​Hands-On Artificial Intelligence for IoT

​Hands-On Artificial Intelligence for IoT

5 (1)
By: Dr. Amita Kapoor

Overview of this book

Transform IoT devices into intelligent systems with this comprehensive guide by Amita Kapoor, Chief AI Officer at Tipz AI. Drawing on 25 years of expertise in developing intelligent systems across industries, she demonstrates how to harness the combined power of artificial intelligence and IoT technology. A pioneer in making AI and neuroscience education accessible worldwide, Amita guides you through creating smart, efficient systems that leverage the latest advances in both fields. This new edition is updated with various optimization techniques in IoT used for enhancing efficiency and performance. It introduces you to cloud platforms such as Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) for analyzing data generated using IoT devices. You’ll learn about machine learning algorithms, deep learning techniques, and practical applications in real-world IoT scenarios and advance to creating AI models that work with diverse data types, including time series, images, and audio. You’ll also harness the power of widely used Python libraries, TensorFlow and Keras, to build a variety of smart AI models. *Email sign-up and proof of purchase required
Table of Contents (23 chapters)
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Part 1: Foundations and Basic Integrations of IoT and AI
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Part 2: Advanced AI Techniques and Their Application in IoT
10
Part 3: Implementing Intelligent IoT Solutions in Diverse Domains
16
Part 4: Applying AI and IoT in Real-World Scenarios

Summary

This chapter focused on handling and analyzing non-visual IoT data – specifically text, audio, and speech. It began by highlighting the importance of textual alerts, logs, and voice commands in IoT ecosystems, emphasizing how these modalities provide more intuitive user interfaces and richer context than raw numeric sensor readings. We covered fundamental preprocessing steps such as normalization, tokenization, stopword removal, and lemmatization, ensuring that textual data is clean and ready for analysis. We also introduced various encoding methods, from simple one-hot vectors to advanced contextual embeddings such as BERT, enabling more robust NLU.

Beyond text, we delved into audio processing and feature extraction. We covered how to classify audio commands, leverage speech recognition to transcribe spoken requests, and apply NLU to interpret them. The integration of voice assistants and IoT APIs was also discussed, showing how ASR, NLU, and device control can create...

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