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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 (22 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

In this chapter, we learned about RL and how it’s different from supervised and unsupervised learning. The emphasis of this chapter was on DRL, where deep neural networks are used to approximate the policy function, the value function, or even both. This chapter introduced OpenAI Gym, a library that provides a large number of environments to train RL agents. We learned about value-based methods such as Q- learning and used it to train an agent to pick up and drop passengers off in a taxi. We also used a DQN to train an agent to play an Atari game. This chapter then moved on to policy-based methods, specifically policy gradients. We covered the intuition behind policy gradients and used the algorithm to train a bipedal robot to walk using DDPG.

In the next chapter, we’ll explore generative models and learn the secrets behind generative adversarial networks.

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