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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
5
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

In this chapter, we explored the evolving role of AI in processing visual data within IoT ecosystems. Visual data presents both unique opportunities and challenges, requiring advanced AI techniques for efficient processing and meaningful analysis.

We began by examining the unique role of visual data in IoT, distinguishing it from other IoT data types, such as time series or tabular data.

Then, we discussed the high volume and redundancy in video streams, hardware constraints on resource-limited IoT devices, and privacy risks associated with sensitive visual data. Strategies such as data compression, lightweight models, encryption, and ethical AI practices were highlighted as key enablers for mitigating these challenges.

The core of this chapter focused on leveraging AI techniques for visual data in IoT and introduced powerful architectures such as MobileNet, EfficientNet, and YOLO for object detection and classification in real-time, resource-constrained environments...

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