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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

Frameworks for EAs

Python, with its rich scientific computing ecosystem, provides several excellent frameworks that streamline the implementation and experimentation with EAs. In this section, we’ll delve into two popular choices: PyGAD and distributed EAs in Python (DEAP).

PyGAD – GA Python library

PyGAD is a user-friendly library built on top of NumPy, emphasizing simplicity for both practitioners and researchers. Its design allows for easy customization of various EA components. It supports a wide range of genetic operators (selection, crossover, mutation). It also provides flexibility to create custom operators. It integrates seamlessly with ML libraries such as PyTorch, TensorFlow, and Keras. It also offers built-in visualization tools for analysis. It is best suited when ease of use, flexibility in defining operators, and integration with other Python libraries are important considerations.

DEAP – distributed EAs in Python

DEAP is a mature and...

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