Deep learning involves multiple layers of neural networks for complex tasks from "summary" of Machine Learning by Ethem Alpaydin
Deep learning is a subfield of machine learning that is concerned with algorithms inspired by the structure and function of the brain known as artificial neural networks. These neural networks are made up of interconnected nodes, or artificial neurons, that are organized into layers. In traditional neural networks, there is typically only one hidden layer between the input and output layers. However, deep learning involves the use of multiple hidden layers, hence the term "deep" learning. By incorporating multiple layers of neurons, deep learning models are able to learn increasingly complex representations of data. Each layer in a deep neural network learns to identify different features or patterns in the input data. As the data passes through each layer, it undergoes a series of transformations that allow the network to extract higher-level features and representations. This hierarchical learning process enables deep learning models to tackle more complex tasks that may be beyond the capabilities of shallow neural networks. The ability...
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