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Book Overview & Buying
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Table Of Contents
Hands-On Artificial Intelligence for IoT - Second Edition
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In Chapters 3 and 4, we laid the groundwork for understanding Machine Learning (ML) and Deep Learning (DL), diving into various algorithms and neural network architectures that form the backbone of modern AI solutions. Specifically, we explored Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks as powerful tools for handling sequential data due to their ability to capture time-dependent patterns. Building on that foundation, this chapter focuses specifically on applying these DL models to time-series data generated from IoT devices.
Time-series data is at the heart of IoT applications, driving everything from the real-time monitoring of industrial equipment to predictive maintenance in smart cities. Given the volume and velocity of this data, scalable approaches are crucial. This chapter not only revisits RNNs and LSTMs, applying them to IoT data, but also introduces SparkML, a framework designed to handle large...