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Of course, this does not completely eliminate the 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.

More precisely, deep learning systems have a substantial credit assignment path CAP depth. The CAP is the chain of transformations from input to output.

CAPs describe potentially causal connections between input and output. For a feedforward neural network , the depth of the CAPs is that of the network and is the number of hidden layers plus one as the output layer is also parameterized.

For recurrent neural networks , in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited.

CAP of depth 2 has been shown to be a universal approximator in the sense that it can emulate any function. Deep learning architectures can be constructed with a greedy layer-by-layer method.

For supervised learning tasks, deep learning methods eliminate feature engineering , by translating the data into compact intermediate representations akin to principal components , and derive layered structures that remove redundancy in representation.

Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data are more abundant than the labeled data.

Examples of deep structures that can be trained in an unsupervised manner are neural history compressors [16] and deep belief networks.

Deep neural networks are generally interpreted in terms of the universal approximation theorem [18] [19] [20] [21] [22] or probabilistic inference.

The classic universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions.

The universal approximation theorem for deep neural networks concerns the capacity of networks with bounded width but the depth is allowed to grow.

Lu et al. The probabilistic interpretation [23] derives from the field of machine learning. It features inference, [11] [12] [1] [2] [17] [23] as well as the optimization concepts of training and testing , related to fitting and generalization , respectively.

More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function.

The first general, working learning algorithm for supervised, deep, feedforward, multilayer perceptrons was published by Alexey Ivakhnenko and Lapa in The term Deep Learning was introduced to the machine learning community by Rina Dechter in , [30] [16] and to artificial neural networks by Igor Aizenberg and colleagues in , in the context of Boolean threshold neurons.

In , Yann LeCun et al. While the algorithm worked, training required 3 days. By such systems were used for recognizing isolated 2-D hand-written digits, while recognizing 3-D objects was done by matching 2-D images with a handcrafted 3-D object model.

Weng et al. Because it directly used natural images, Cresceptron started the beginning of general-purpose visual learning for natural 3D worlds.

Cresceptron is a cascade of layers similar to Neocognitron. But while Neocognitron required a human programmer to hand-merge features, Cresceptron learned an open number of features in each layer without supervision, where each feature is represented by a convolution kernel.

Cresceptron segmented each learned object from a cluttered scene through back-analysis through the network. Max pooling , now often adopted by deep neural networks e.

ImageNet tests , was first used in Cresceptron to reduce the position resolution by a factor of 2x2 to 1 through the cascade for better generalization.

Each layer in the feature extraction module extracted features with growing complexity regarding the previous layer.

In , Brendan Frey demonstrated that it was possible to train over two days a network containing six fully connected layers and several hundred hidden units using the wake-sleep algorithm , co-developed with Peter Dayan and Hinton.

Simpler models that use task-specific handcrafted features such as Gabor filters and support vector machines SVMs were a popular choice in the s and s, because of artificial neural network 's ANN computational cost and a lack of understanding of how the brain wires its biological networks.

Both shallow and deep learning e. Most speech recognition researchers moved away from neural nets to pursue generative modeling.

An exception was at SRI International in the late s. The speaker recognition team led by Larry Heck reported significant success with deep neural networks in speech processing in the National Institute of Standards and Technology Speaker Recognition evaluation.

The principle of elevating "raw" features over hand-crafted optimization was first explored successfully in the architecture of deep autoencoder on the "raw" spectrogram or linear filter-bank features in the late s, [52] showing its superiority over the Mel-Cepstral features that contain stages of fixed transformation from spectrograms.

The raw features of speech, waveforms , later produced excellent larger-scale results. Many aspects of speech recognition were taken over by a deep learning method called long short-term memory LSTM , a recurrent neural network published by Hochreiter and Schmidhuber in In , LSTM started to become competitive with traditional speech recognizers on certain tasks.

In , publications by Geoff Hinton , Ruslan Salakhutdinov , Osindero and Teh [59] [60] [61] showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine , then fine-tuning it using supervised backpropagation.

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

The NIPS Workshop on Deep Learning for Speech Recognition [72] was motivated by the limitations of deep generative models of speech, and the possibility that given more capable hardware and large-scale data sets that deep neural nets DNN might become practical.

It was believed that pre-training DNNs using generative models of deep belief nets DBN would overcome the main difficulties of neural nets.

DNN models, stimulated early industrial investment in deep learning for speech recognition, [75] [72] eventually leading to pervasive and dominant use in that industry.

That analysis was done with comparable performance less than 1. In , researchers extended deep learning from TIMIT to large vocabulary speech recognition, by adopting large output layers of the DNN based on context-dependent HMM states constructed by decision trees.

Advances in hardware have driven renewed interest in deep learning. While there, Andrew Ng determined that GPUs could increase the speed of deep-learning systems by about times.

In , a team led by George E. Dahl won the "Merck Molecular Activity Challenge" using multi-task deep neural networks to predict the biomolecular target of one drug.

Significant additional impacts in image or object recognition were felt from to In October , a similar system by Krizhevsky et al.

In November , Ciresan et al. The Wolfram Image Identification project publicized these improvements. Image classification was then extended to the more challenging task of generating descriptions captions for images, often as a combination of CNNs and LSTMs.

Some researchers state that the October ImageNet victory anchored the start of a "deep learning revolution" that has transformed the AI industry.

In March , Yoshua Bengio , Geoffrey Hinton and Yann LeCun were awarded the Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

Artificial neural networks ANNs or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains.

Such systems learn progressively improve their ability to do tasks by considering examples, generally without task-specific programming.

For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the analytic results to identify cats in other images.

They have found most use in applications difficult to express with a traditional computer algorithm using rule-based programming. An ANN is based on a collection of connected units called artificial neurons , analogous to biological neurons in a biological brain.

Each connection synapse between neurons can transmit a signal to another neuron. The receiving postsynaptic neuron can process the signal s and then signal downstream neurons connected to it.

Neurons may have state, generally represented by real numbers , typically between 0 and 1. Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream.

Typically, neurons are organized in layers. Different layers may perform different kinds of transformations on their inputs.

Signals travel from the first input , to the last output layer, possibly after traversing the layers multiple times. The original goal of the neural network approach was to solve problems in the same way that a human brain would.

Over time, attention focused on matching specific mental abilities, leading to deviations from biology such as backpropagation, or passing information in the reverse direction and adjusting the network to reflect that information.

Neural networks have been used on a variety of tasks, including computer vision, speech recognition , machine translation , social network filtering, playing board and video games and medical diagnosis.

As of , neural networks typically have a few thousand to a few million units and millions of connections.

Despite this number being several order of magnitude less than the number of neurons on a human brain, these networks can perform many tasks at a level beyond that of humans e.

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 [2] 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: [11] [78] [76].

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.

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From the Cambridge English Corpus. Deep learning was enhanced by the sequencing and integration of musikdidaktik, principal instrument and practical teacher training.

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.

Hence, the possibilities for enhancing deep learning in musikdidaktik also seemed embedded outside the subject, yet within the institutional culture itself.

One important interest in this school is the relationship between deep learning and valued language in academic disciplines.

The preconditions for deep learning that were identified as located to the trainees included the areas of trainee identity, learning orientations and musical experience.

The position of the musikdidaktik subject within music teacher education as a whole was also perceived to influence the possibility of deep learning.

The preconditions that were perceived as significant for deep learning included professors' attitudes to the trainees' preconditions and professors' approaches to the subject.

The interviewees particularly emphasised the institution's disposition towards time allocation, and its organisation as being significant for the trainees' deep learning.

It was held that trainee preconditions for deep learning in musikdidaktik were strongly related to trainees' musical maturity. Summing up, three issues that could be connected to the professors emerged as significant for trainees to achieve deep learning.

Summing up, three time-related issues emerged as significant for the trainees' deep learning. The professor-content relation of the didaktik triangle was perceived to include preconditions for the trainees' deep learning in several significant ways.

Summing up, five issues that concerned the institutions' educational organisation emerged as significant for the trainees' deep learning.

Another, institutional precondition that seemed to influence trainees' deep learning rather strongly were the relations between the musikdidaktik subject and practical teacher training.

This made it impossible for trainees to achieve deep learning in all subjects. See all examples of deep learning. What is the pronunciation of deep learning?

Browse deep ecology BETA. It features inference, [11] [12] [1] [2] [17] [23] as well as the optimization concepts of training and testing , related to fitting and generalization , respectively.