Deep Learning
Deep learning is a subfield of machine learning concerned with the artificial neural networks that are inspired by the function and structure of the brain. Deep networks have attracted much attention from many application domains since the remarkable improvements in image classification and speech recognition, leading to the recent revival of artificial neural networks.

Deep networks are deeper, larger, and more complex artificial neural networks than traditional ones. Deep networks usually consist of more than twenty layers while traditional ones have just two or three layers. The introduction of GPUs (Graphic Processing Units) made it possible to accelerate the learning process of deep networks, that otherwise would take a huge amount of time due to the large number of parameters in deep networks, dramatically.

The most popular deep networks are Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), and autoencoders. CNNs have phenomenal performance in computer vision tasks such as image classification and recognition. The architecture of CNNs, which models the human visual system, allows us to almost eliminate the feature engineering that are crucial and inevitable to the performance in computer vision tasks. CNNs become the de facto standard in image recognition and understanding. DBNs are graphical models that are capable of learning reconstruction of its inputs probabilistically, recently adopted in drug discovery because they allow efficient search and optimization in the space of chemical compounds.
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