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This chapter will cover the basics of artificial neural networks (ANNs) which are also called multilayer perceptrons. Neural networks are networks of interconnected artificial neurons. Their structure is heavily inspired by the brain’s neuron network. A neural network is generally used to create supervised machine learning models for classification, similar to a Logistic Regression model, and is useful in cases where Logistic Regression may not provide reasonable accuracy. Neural networks form the basis of many of the complex applications and algorithms of machine learning. You will subsequently see some of these applications in Chaps. 16 and 17 (Reinforcement Learning), 11 (Recurrent Neural Network Used in Language Processing), and 15 (Convolutional Neural Network). A good understanding of a neural network is necessary to understand these and other applications that have raised so much interest in machine learning. Neural networks are also used in unsupervised learning for compressed representation and/or dimensionality reduction.
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