Chapter 3. Artificial Neural Networks
There are many different types of models used in machine learning. However, one class of machine learning models that stands out is artificial neural networks (ANNs). Given that artificial neural networks are used across all machine learning types, this chapter will cover the basics of ANNs.
ANNs are computing systems based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it.
Deep learning involves the study of complex ANN-related algorithms. The complexity is attributed to elaborate patterns of how information flows throughout the model. Deep learning has the ability to represent the world as a nested hierarchy of concepts, with each concept defined in relation to a simpler concept. Deep learning techniques are extensively used in reinforcement learning and natural language processing applications that we will look at in Chapters 9 and 10.
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