a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hidden layers), but also with some sort of Probabilistic Graphical Models.
Deep architectures allow more complex tasks to be learned because, in addition to these neural networks having more layers to perform transformations, the larger number of layers and more complex architectures of the neural network allow a hierarchical organization of functionality to emerge.
Deep Learning was introduced into machine learning research with the intention of moving machine learning closer to artificial intelligence. A significant impact of deep learning lies in feature learning, mitigating much of the effort going into manual feature engineering in non-deep learning neural networks.
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