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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 this chapter, we covered some basic and useful deep neural network models. We started with a single neuron and saw its power and its limitations. The MLP was built for both regression and classification tasks. We also introduced the backpropagation algorithm. The chapter progressed to CNN, with an introduction to the convolution layers and pooling layers. We learned about some of the successful CNNs and used the first CNN, LeNet, to perform handwritten digit recognition. From the feed-forward MLPs and CNNs, we moved on to RNNs. We introduced LSTM and GRU networks. We learned about autoencoders and, finally, were introduced to the OpenVino and TinyML frameworks.
In the next chapter, we will start with a totally new type of AI model genetic algorithm. Like neural networks, it too is inspired by nature. We will use what we learned in this chapter and some of the upcoming chapters in the case studies we’ll do later in the book.