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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

This was an interesting chapter, and I hope you enjoyed reading it as much as I enjoyed writing it. A lot is happening in the generative AI space, and in this chapter, we could only scratch the surface. This chapter introduced generative models and their classification, namely implicit generative models and explicit generative models. The first generative model that was covered is VAEs; they’re an explicit generative model and try to estimate the lower bound on the density function. The VAEs were implemented in Keras and were used for anomaly detection.

This chapter then moved on to a more popular explicit generative model: GANs. The GAN architecture, especially how the discriminator network and generative network compete with each other, was explained. Multiple variations of GANs and their special features were covered. We implemented a conditional GAN using Keras for generating handwritten digits. Lastly, we delved into KerasNLP and explored pre-trained language...

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