Most often, the term deep learning is used to describe artificial neural networks that were designed to work with large amounts of data and use complex algorithms to train the model. Algorithms for deep learning can use both supervised and unsupervised algorithms (reinforcement learning). The learning process is deep because, over time, the neural network covers an increasing number of levels. The deeper the network is (that is, it has more hidden layers, filters, and levels of feature abstraction it has), the higher the network's performance. On large datasets, deep learning shows better accuracy than traditional machine learning algorithms.
The real breakthrough that led to the current resurgence of interest in deep neural networks occurred in 2012, after the publication of the article ImageNet classification with deep convolutional neural networks...
