Deep learning - Wikipedia. Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non- linear transformations. An observation (e. Some representations are better than others at simplifying the learning task (e. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi- supervisedfeature learning and hierarchical feature extraction. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). Higher level features are derived from lower level features to form a hierarchical representation. These definitions have in common (1) multiple layers of nonlinear processing units and (2) the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low- level to high- level features. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of complicated propositional formulas. At each layer, the signal is transformed by a processing unit, like an artificial neuron, whose parameters are 'learned' through training. CAPs describe potentially causal connections between input and output and may vary in length. For a feedforward neural network, the depth of the CAPs, and thus the depth of the network, is the number of hidden layers plus one (the output layer is also parameterized). For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP is potentially unlimited in length. There is no universally agreed upon threshold of depth dividing shallow learning from deep learning, but most researchers in the field agree that deep learning has multiple nonlinear layers (CAP > 2) and Juergen Schmidhuber considers CAP > 1. The underlying assumption behind distributed representations is that observed data are generated by the interactions of factors organized in layers. Deep learning adds the assumption that these layers of factors correspond to levels of abstraction or composition. Diabetes Ketoacidosis Treatment Diabetes & Alternative Diabetes Treatment This is the preprint of an invited Deep Learning (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve. We've finally made it. We've made it to what we've all been waiting for, Q-learning with neural networks. Since I'm sure a lot of people. LETTER doi:10.1038/nature14236 Human-level control through deep reinforcement learning Volodymyr Mnih1*, Koray Kavukcuoglu1*, David Silver1*, Andrei A. Rusu1, Joel Veness1, Marc G. Bellemare1, Alex Graves1, Martin Riedmiller 1. The Machine Learning Salon provides free information about Machine Learning and Artificial Intelligence. Its aim is to develop the understanding of Machine Learning Theory and its applications by providing a first. Varying numbers of layers and layer sizes can be used to provide different amounts of abstraction. These architectures are often constructed with a greedy layer- by- layer method. Deep learning helps to disentangle these abstractions and pick out which features are useful for learning. This is an important benefit because unlabeled data are usually more abundant than labeled data. Examples of deep structures that can be trained in an unsupervised manner are neural history compressors. It features inference. More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function. The probabilistic interpretation led to the introduction of dropout as regularizer in neural networks. Other Deep Learning working architectures, specifically those built from artificial neural networks (ANN), date back to the Neocognitron introduced by Kunihiko Fukushima in 1. The challenge was how to train networks with multiple layers. In 1. 98. 9, Yann Le. Cun et al. Despite the success of applying the algorithm, the time to train the network on this dataset was approximately 3 days, making it impractical for general use. Cresceptron is a cascade of layers similar to Neocognitron. But while Neocognitron required a human programmer to hand- merge features, Cresceptron automatically learned an open number of unsupervised features in each layer, where each feature is represented by a convolution kernel. Cresceptron also segmented each learned object from a cluttered scene through back- analysis through the network. Max pooling, now often adopted by deep neural networks (e. Image. Net tests), was first used in Cresceptron to reduce the position resolution by a factor of (2x. Despite these advantages, simpler models that use task- specific handcrafted features such as Gabor filters and support vector machines (SVMs) were a popular choice in the 1. ANNs at the time, and a great lack of understanding of how the brain autonomously wires its biological networks. In the long history of speech recognition, both shallow and deep learning (e. Thus, most speech recognition researchers who understood such barriers moved away from neural nets to pursue generative modeling. An exception was at SRI International in the late 1. Funded by the US government's NSA and DARPA, SRI conducted research on deep neural networks in speech and speaker recognition. The speaker recognition team, led by Larry Heck, achieved the first significant success with deep neural networks in speech processing as demonstrated in the 1. NIST (National Institute of Standards and Technology) Speaker Recognition evaluation and later published in the journal of Speech Communication. In 2. 00. 3, LSTM started to become competitive with traditional speech recognizers on certain tasks. Results on commonly used evaluation sets such as TIMIT (ASR) and MNIST (image classification), as well as a range of large- vocabulary speech recognition tasks are constantly being improved with new applications of deep learning. In late 2. 00. 9, Li Deng invited Geoffrey Hinton to work with him and colleagues at Microsoft Research in Redmond, Washington to apply deep learning to speech recognition. They co- organized the 2. NIPS Workshop on Deep Learning for Speech Recognition. The workshop was motivated by the limitations of deep generative models of speech, and the possibility that the big- compute, big- data era warranted a serious try of deep neural nets (DNN). It was believed that pre- training DNNs using generative models of deep belief nets (DBN) would overcome the main difficulties of neural nets encountered in the 1. This finding was verified by several other major speech recognition research groups. The history of this significant development in deep learning has been described and analyzed in recent books and articles. In particular, powerful graphics processing units (GPUs) are well- suited for the kind of number crunching, matrix/vector math involved in machine learning. Artificial neural networks are inspired by the 1. Nobel laureates. David H. Hubel & Torsten Wiesel, who found two types of cells in the primary visual cortex: simple cells and complex cells. Many artificial neural networks can be viewed as cascading models. Max- pooling appeared to be first proposed by Cresceptron. Max- pooling helps, but does not guarantee, shift- invariance at the pixel level. Sepp Hochreiter's diploma thesis of 1. Recurrent networks are trained by unfolding them into very deep feedforward networks, where a new layer is created for each time step of an input sequence processed by the network. As errors propagate from layer to layer, they shrink exponentially with the number of layers, impeding the tuning of neuron weights which is based on those errors. To overcome this problem, several methods were proposed. Then the network is trained further by supervised back- propagation to classify labeled data. The deep model of Hinton et al. It uses a restricted Boltzmann machine (Smolensky, 1. Each new layer guarantees an increase on the lower- bound of the log likelihood of the data, thus improving the model, if trained properly. Once sufficiently many layers have been learned, the deep architecture may be used as a generative model by reproducing the data when sampling down the model (an . In 2. 01. 0, Dan Ciresan and colleagues. The method outperformed all other machine learning techniques on the old, famous MNIST handwritten digits problem of Yann Le. Cun and colleagues at NYU. At about the same time, in late 2. NIPS Workshop on Deep Learning for Speech Recognition. Intensive collaborative work between Microsoft Research and University of Toronto researchers demonstrated by mid- 2. Redmond that deep neural networks interfaced with a hidden Markov model with context- dependent states that define the neural network output layer can drastically reduce errors in large- vocabulary speech recognition tasks such as voice search. The same deep neural net model was shown to scale up to Switchboard tasks about one year later at Microsoft Research Asia. Even earlier, in 2. LSTM. Training is usually done without any unsupervised pre- training. Since 2. 01. 1, GPU- based implementations. ANNs were able to guarantee shift invariance to deal with small and large natural objects in large cluttered scenes, only when invariance extended beyond shift, to all ANN- learned concepts, such as location, type (object class label), scale, lighting. This was realized in Developmental Networks (DNs). Most of them are branched from some original parent architectures. It is not always possible to compare the performance of multiple architectures all together, because they are not all evaluated on the same data sets. Deep learning is a fast- growing field, and new architectures, variants, or algorithms appear every few weeks. Brief discussion of deep neural networks. DNN architectures, e. According to various sources. In 1. 96. 2, Stuart Dreyfus published a simpler derivation based only on the chain rule. Bryson and Yu- Chi Ho described it as a multi- stage dynamic system optimization method in 1. Rumelhart, Geoffrey E. Williams showed through computer experiments that this method can generate useful internal representations of incoming data in hidden layers of neural networks.
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