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A deep neural network DNN is an artificial neural network ANN with multiple layers between the input and output layers. The network moves through the layers calculating the probability of each output.
For example, a DNN that is trained to recognize dog breeds will go over the given image and calculate the probability that the dog in the image is a certain breed.
The user can review the results and select which probabilities the network should display above a certain threshold, etc. Each mathematical manipulation as such is considered a layer, and complex DNN have many layers, hence the name "deep" networks.
DNNs can model complex non-linear relationships. DNN architectures generate compositional models where the object is expressed as a layered composition of primitives.
Deep architectures include many variants of a few basic approaches. Each architecture has found success in specific domains.
It is not always possible to compare the performance of multiple architectures, unless they have been evaluated on the same data sets.
DNNs are typically feedforward networks in which data flows from the input layer to the output layer without looping back.
At first, the DNN creates a map of virtual neurons and assigns random numerical values, or "weights", to connections between them.
The weights and inputs are multiplied and return an output between 0 and 1. If the network did not accurately recognize a particular pattern, an algorithm would adjust the weights.
Recurrent neural networks RNNs , in which data can flow in any direction, are used for applications such as language modeling.
Convolutional deep neural networks CNNs are used in computer vision. Two common issues are overfitting and computation time.
DNNs are prone to overfitting because of the added layers of abstraction, which allow them to model rare dependencies in the training data.
This helps to exclude rare dependencies. DNNs must consider many training parameters, such as the size number of layers and number of units per layer , the learning rate , and initial weights.
Sweeping through the parameter space for optimal parameters may not be feasible due to the cost in time and computational resources.
Various tricks, such as batching computing the gradient on several training examples at once rather than individual examples  speed up computation.
Large processing capabilities of many-core architectures such as GPUs or the Intel Xeon Phi have produced significant speedups in training, because of the suitability of such processing architectures for the matrix and vector computations.
Alternatively, engineers may look for other types of neural networks with more straightforward and convergent training algorithms.
CMAC cerebellar model articulation controller is one such kind of neural network. It doesn't require learning rates or randomized initial weights for CMAC.
The training process can be guaranteed to converge in one step with a new batch of data, and the computational complexity of the training algorithm is linear with respect to the number of neurons involved.
Since the s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.
Large-scale automatic speech recognition is the first and most convincing successful case of deep learning. 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.
LSTM with forget gates  is competitive with traditional speech recognizers on certain tasks. The data set contains speakers from eight major dialects of American English , where each speaker reads 10 sentences.
More importantly, the TIMIT task concerns phone-sequence recognition, which, unlike word-sequence recognition, allows weak phone bigram language models.
This lets the strength of the acoustic modeling aspects of speech recognition be more easily analyzed.
The error rates listed below, including these early results and measured as percent phone error rates PER , have been summarized since The debut of DNNs for speaker recognition in the late s and speech recognition around and of LSTM around —, accelerated progress in eight major areas:   .
All major commercial speech recognition systems e. MNIST is composed of handwritten digits and includes 60, training examples and 10, test examples.
A comprehensive list of results on this set is available. Deep learning-based image recognition has become "superhuman", producing more accurate results than human contestants.
This first occurred in Closely related to the progress that has been made in image recognition is the increasing application of deep learning techniques to various visual art tasks.
DNNs have proven themselves capable, for example, of a identifying the style period of a given painting, b Neural Style Transfer - capturing the style of a given artwork and applying it in a visually pleasing manner to an arbitrary photograph or video, and c generating striking imagery based on random visual input fields.
Neural networks have been used for implementing language models since the early s. Other key techniques in this field are negative sampling  and word embedding.
Word embedding, such as word2vec , can be thought of as a representational layer in a deep learning architecture that transforms an atomic word into a positional representation of the word relative to other words in the dataset; the position is represented as a point in a vector space.
Using word embedding as an RNN input layer allows the network to parse sentences and phrases using an effective compositional vector grammar.
Recent developments generalize word embedding to sentence embedding. Google Translate GT uses a large end-to-end long short-term memory network.
Google Translate supports over one hundred languages. A large percentage of candidate drugs fail to win regulatory approval. These failures are caused by insufficient efficacy on-target effect , undesired interactions off-target effects , or unanticipated toxic effects.
AtomNet is a deep learning system for structure-based rational drug design. In generative neural networks were used to produce molecules that were validated experimentally all the way into mice.
Deep reinforcement learning has been used to approximate the value of possible direct marketing actions, defined in terms of RFM variables.
The estimated value function was shown to have a natural interpretation as customer lifetime value. Recommendation systems have used deep learning to extract meaningful features for a latent factor model for content-based music and journal recommendations.
An autoencoder ANN was used in bioinformatics , to predict gene ontology annotations and gene-function relationships.
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  .
Finding the appropriate mobile audience for mobile advertising is always challenging, since many data points must be considered and analyzed before a target segment can be created and used in ad serving by any ad server.
This information can form the basis of machine learning to improve ad selection. 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 and anti-money laundering.
The solution leverages both supervised learning techniques, such as the classification of suspicious transactions, and unsupervised learning, e.
The United States Department of Defense applied deep learning to train robots in new tasks through observation. Deep learning is closely related to a class of theories of brain development specifically, neocortical development proposed by cognitive neuroscientists in the early s.
These developmental models share the property that various proposed learning dynamics in the brain e. Like the neocortex , neural networks employ a hierarchy of layered filters in which each layer considers information from a prior layer or the operating environment , and then passes its output and possibly the original input , to other layers.
This process yields a self-organizing stack of transducers , well-tuned to their operating environment. A description stated, " A variety of approaches have been used to investigate the plausibility of deep learning models from a neurobiological perspective.
On the one hand, several variants of the backpropagation algorithm have been proposed in order to increase its processing realism.
Although a systematic comparison between the human brain organization and the neuronal encoding in deep networks has not yet been established, several analogies have been reported.
For example, the computations performed by deep learning units could be similar to those of actual neurons   and neural populations.
Facebook 's AI lab performs tasks such as automatically tagging uploaded pictures with the names of the people in them.
Google's DeepMind Technologies developed a system capable of learning how to play Atari video games using only pixels as data input.
In they demonstrated their AlphaGo system, which learned the game of Go well enough to beat a professional Go player. In , Blippar demonstrated a mobile augmented reality application that uses deep learning to recognize objects in real time.
In , Covariant. As of ,  researchers at The University of Texas at Austin UT developed a machine learning framework called Training an Agent Manually via Evaluative Reinforcement, or TAMER, which proposed new methods for robots or computer programs to learn how to perform tasks by interacting with a human instructor.
Deep learning has attracted both criticism and comment, in some cases from outside the field of computer science.
A main criticism concerns the lack of theory surrounding some methods. However, the theory surrounding other algorithms, such as contrastive divergence is less clear.
If so, how fast? What is it approximating? Deep learning methods are often looked at as a black box , with most confirmations done empirically, rather than theoretically.
Others point out that deep learning should be looked at as a step towards realizing strong AI, not as an all-encompassing solution. Despite the power of deep learning methods, they still lack much of the functionality needed for realizing this goal entirely.
Research psychologist Gary Marcus noted:. Such techniques lack ways of representing causal relationships The most powerful A.
In further reference to the idea that artistic sensitivity might inhere within relatively low levels of the cognitive hierarchy, a published series of graphic representations of the internal states of deep layers neural networks attempting to discern within essentially random data the images on which they were trained  demonstrate a visual appeal: the original research notice received well over 1, comments, and was the subject of what was for a time the most frequently accessed article on The Guardian 's  website.
Some deep learning architectures display problematic behaviors,  such as confidently classifying unrecognizable images as belonging to a familiar category of ordinary images  and misclassifying minuscule perturbations of correctly classified images.
As deep learning moves from the lab into the world, research and experience shows that artificial neural networks are vulnerable to hacks and deception.
For example, an attacker can make subtle changes to an image such that the ANN finds a match even though the image looks to a human nothing like the search target.
The modified images looked no different to human eyes. Another group showed that printouts of doctored images then photographed successfully tricked an image classification system.
A refinement is to search using only parts of the image, to identify images from which that piece may have been taken. Another group showed that certain psychedelic spectacles could fool a facial recognition system into thinking ordinary people were celebrities, potentially allowing one person to impersonate another.
In researchers added stickers to stop signs and caused an ANN to misclassify them. ANNs can however be further trained to detect attempts at deception, potentially leading attackers and defenders into an arms race similar to the kind that already defines the malware defense industry.
ANNs have been trained to defeat ANN-based anti-malware software by repeatedly attacking a defense with malware that was continually altered by a genetic algorithm until it tricked the anti-malware while retaining its ability to damage the target.
Another group demonstrated that certain sounds could make the Google Now voice command system open a particular web address that would download malware.
It has been argued in media philosophy that not only low-paid clickwork e. For this purpose Facebook introduced the feature that once a user is automatically recognized in an image, they receive a notification.
They can choose whether of not they like to be publicly labeled on the image, or tell Facebook that it is not them in the picture.
As Mühlhoff argues, involvement of human users to generate training and verification data is so typical for most commercial end-user applications of Deep Learning that such systems may be referred to as "human-aided artificial intelligence".
From Wikipedia, the free encyclopedia. For deep versus shallow learning in educational psychology, see Student approaches to learning.
For more information, see Artificial neural network. Branch of machine learning. Dimensionality reduction. Structured prediction. Graphical models Bayes net Conditional random field Hidden Markov.
Anomaly detection. Artificial neural network. Reinforcement learning. Machine-learning venues. Glossary of artificial intelligence.
Related articles. List of datasets for machine-learning research Outline of machine learning. Major goals. Knowledge reasoning Planning Machine learning Natural language processing Computer vision Robotics Artificial general intelligence.
Symbolic Deep learning Bayesian networks Evolutionary algorithms. Timeline Progress AI winter.
You can also find related words, phrases, and synonyms in the topics: People in charge of or controlling other people.
He was promoted to the rank of general. The revolt in the north is believed to have been instigated by a high-ranking general.
He's a competent enough officer , but I doubt he'll ever make general. The general mustered his troops. The general has ordered a partial withdrawal of troops from the area.
The general made some bellicose statements about his country's military strength. Ranks in the Army. Only a third of the general population are willing to haggle over the price of something they want to buy.
Market demand for all our products remains strong , reflecting continued growth in the general economy. Let me describe the finances in general terms without being specific.
Mike Black , general manager at the plant , said only a small percentage of the workforce were members of the union.
In general, British management style is known for its individuality. See also attorney general. Examples of general.
The above concept is very general and idealistic. From the Cambridge English Corpus. The solutions of the equations 3.
These examples are from the Cambridge English Corpus and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors.
But there is no general history of the magazine. However, this is not simply a matter of the "identification and denigration of differences" in general.
Alternatively, one can propose a general licensing principle that permits such violations at that level. In general , the laser-heated plasma volume is smaller than the cluster plume.
In observational research in the general population, however, a continuum approach may be more useful than use of a qualitative cut-off point.
A qualitative argument is derived to discriminate between oscillatory and stationary onset of instability in the general case.
He surveys the sacred meals against a carefully assembled background of ordinary meal-taking and the general philosophy of food in each region.
The citizens of the country constitute an organic whole, which is integrated by the general will of the masses. In general , little is known about those living on the margins of society during this period, especially in the rural regions.
In general , reflexive pronouns do not form a large percentage of postverbal pronouns. The findings underline the importance of using specific rather than general tasks to assess phonology, phonological sensitivity, and phonological representations.
Such reports highlight consequences that affect development prospects in general. However patient participation activities were reported to be variable, dependent on the motivation and interest of individual general practices.
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