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Deep learning also known as deep structured learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervisedsemi-supervised or unsupervised.

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Deep-learning architectures such as deep neural networksdeep belief networksrecurrent neural networks and convolutional neural networks have been applied to fields including computer visionmachine visionspeech recognitionnatural language processingaudio recognitionsocial network filtering, machine translationbioinformaticsdrug designmedical image analysismaterial inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Artificial neural networks ANNs were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static more info symbolic, while the biological brain of most living organisms is dynamic plastic and analogue.

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The adjective "deep" in deep learning refers to the use of multiple layers in the network. Early work showed that a linear perceptron cannot be a universal classifier, and then that a network with a nonpolynomial activation function with one hidden layer of unbounded width can on the other hand so be.

Deep learning is a modern variation which is concerned with an unbounded number of layers of bounded size, which permits practical application and optimized implementation, while retaining theoretical universality under mild conditions. In deep learning the layers are also permitted to be heterogeneous and to deviate widely from biologically informed connectionist models, A Brief Note On Systemic Approach And the sake of efficiency, trainability and understandability, whence the "structured" part.

Deep learning is a class of machine learning algorithms that [12] pp— uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processinglower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. Most modern deep learning models are based on artificial neural networksspecifically convolutional neural networks CNN s, although they can also include propositional formulas or latent variables organized layer-wise in deep generative Aplroach such as the nodes in deep belief networks and deep Boltzmann machines. In deep learning, each level learns to transform its input data into a source more abstract Brife composite representation.

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In an image recognition application, the raw input may be a matrix of pixels; the first representational layer may abstract the pixels and encode edges; the second layer may compose and encode arrangements of edges; the third layer may encode a nose and eyes; and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place in which level on its own.

Systekic course, this does not completely eliminate continue reading need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction.

The word "deep" in "deep learning" refers to the number of layers through which the data is transformed.]