33 Facts About Deep learning

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning.

Adjective "deep" in deep learning refers to the use of multiple layers in the network.

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.

Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input.

Deep learning helps to disentangle these abstractions and pick out which features improve performance.

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Deep learning described it in his book "Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms", published by Cornell Aeronautical Laboratory, Inc, Cornell University in 1962.

Term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to artificial neural networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.

Many aspects of speech recognition were taken over by a deep learning method called long short-term memory, a recurrent neural network published by Hochreiter and Schmidhuber in 1997.

LSTM RNNs avoid the vanishing gradient problem and can learn "Very Deep Learning" tasks that require memories of events that happened thousands of discrete time steps before, which is important for speech.

Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision and automatic speech recognition .

In 2014, Hochreiter's group used deep learning to detect off-target and toxic effects of environmental chemicals in nutrients, household products and drugs and won the "Tox21 Data Challenge" of NIH, FDA and NCATS.

In 2013 and 2014, the error rate on the ImageNet task using deep learning was further reduced, following a similar trend in large-scale speech recognition.

Deep learning neural network is an artificial neural network with multiple layers between the input and output layers.

Deep learning architectures include many variants of a few basic approaches.

Special electronic circuits called deep learning processors were designed to speed up deep learning algorithms.

Deep learning processors include neural processing units in Huawei cellphones and cloud computing servers such as tensor processing units in the Google Cloud Platform.

LSTM RNNs can learn "Very Deep Learning" tasks that involve multi-second intervals containing speech events separated by thousands of discrete time steps, where one time step corresponds to about 10 ms.

Deep learning-based image recognition has become "superhuman", producing more accurate results than human contestants.

Research has explored use of deep learning to predict the biomolecular targets, off-targets, and toxic effects of environmental chemicals in nutrients, household products and drugs.

Deep reinforcement learning has been used to approximate the value of possible direct marketing actions, defined in terms of RFM variables.

Recommendation systems have used deep learning to extract meaningful features for a latent factor model for content-based music and journal recommendations.

Multi-view deep learning has been applied for learning user preferences from multiple domains.

In medical informatics, deep learning was used to predict sleep quality based on data from wearables and predictions of health complications from electronic health record data.

Deep learning has been shown to produce competitive results in medical application such as cancer cell classification, lesion detection, organ segmentation and image enhancement.

Deep learning has been used to interpret large, many-dimensioned advertising datasets.

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Deep learning has been successfully applied to inverse problems such as denoising, super-resolution, inpainting, and film colorization.

Deep learning is being successfully applied to financial fraud detection, tax evasion detection, and anti-money laundering.

Also of note is that while the state of the art model in automated financial crime detection has existed for quite some time, the applications for deep learning referred to here dramatically under perform much simpler theoretical models.

Deep learning is closely related to a class of theories of brain development proposed by cognitive neuroscientists in the early 1990s.

Similarly, the representations developed by deep learning models are similar to those measured in the primate visual system both at the single-unit and at the population levels.

Deep TAMER used deep learning to provide a robot the ability to learn new tasks through observation.

Deep learning has attracted both criticism and comment, in some cases from outside the field of computer science.

Muhlhoff argues that in most commercial end-user applications of Deep Learning such as Facebook's face recognition system, the need for training data does not stop once an ANN is trained.