https://en.wikipedia.org/wiki/Deep_learning
Related: Machine Learning • à mettre en ligneRecurrent neural network A recurrent neural network (RNN) is a class of neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. This makes them applicable to tasks such as unsegmented connected handwriting recognition, where they have achieved the best known results.[1]
Self-organizing map A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. Self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map or network.[1][2] Like most artificial neural networks, SOMs operate in two modes: training and mapping.
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Artificial neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated.
Neural Network Package Torch7 This package provides an easy way to build and train simple or complex neural networks. Each module of a network is composed of Modules and there are several sub-classes of Module available: container classes like Sequential, Parallel and Concat , which can contain simple layers like Linear, Mean, Max and Reshape, as well as convolutional layers, and transfer functions like Tanh. Loss functions are implemented as sub-classes of Criterion.
Google leads $542 million funding of mysterious augmented reality firm Magic Leap Google is leading a huge $542 million round of funding for the secretive startup Magic Leap, which is said to be working on augmented reality glasses that can create digital objects that appear to exist in the world around you. Though little is known about what Magic Leap is working on, Google is placing a big bet on it: in addition to the funding, Android and Chrome leader Sundar Pichai will join Magic Leap's board, as will Google's corporate development vice-president Don Harrison. The funding is also coming directly from Google itself — not from an investment arm like Google Ventures — all suggesting this is a strategic move to align the two companies and eventually partner when the tech is more mature down the road. "You’re in the room, and there’s a dragon flying around, it’s jaw-dropping."
Dimensionality reduction In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration,[1] and can be divided into feature selection and feature extraction.[2] Feature selection[edit] Feature extraction[edit] The main linear technique for dimensionality reduction, principal component analysis, performs a linear mapping of the data to a lower-dimensional space in such a way that the variance of the data in the low-dimensional representation is maximized. In practice, the correlation matrix of the data is constructed and the eigenvectors on this matrix are computed. The eigenvectors that correspond to the largest eigenvalues (the principal components) can now be used to reconstruct a large fraction of the variance of the original data.
Restricted Boltzmann machine Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units). A restricted Boltzmann machine (RBM) is a generative stochastic neural network that can learn a probability distribution over its set of inputs. RBMs were initially invented under the name Harmonium by Paul Smolensky in 1986,[1] but only rose to prominence after Geoffrey Hinton and collaborators invented fast learning algorithms for them in the mid-2000s. RBMs have found applications in dimensionality reduction,[2] classification,[3] collaborative filtering, feature learning[4] and topic modelling.[5] They can be trained in either supervised or unsupervised ways, depending on the task. Restricted Boltzmann machines can also be used in deep learning networks.
This Is The Demo That Magic Leap Was Going To Show At TED Before It Backed Out Virtual reality company Magic Leap has been eerily quiet since it announced its $542 million fundraising round last October, with heavyweights like Andreessen Horowitz, Kleiner Perkins, and Google all participating. Now, for the first time in months, we finally have another glimpse of what the Florida-based VR startup has been cooking up in secret. This is the video of a real-world, first-person shooting game that Magic Leap says it was going to show at TED this week, before the company pulled out for reasons that are unclear. (Magic Leap declined to speak with the press about its absence.) It has lasers and robots and enough explosions to make Michael Bay shed a single, lens-flaring tear:
Online machine learning Online machine learning is used in the case where the data becomes available in a sequential fashion, in order to determine a mapping from the dataset to the corresponding labels. The key difference between online learning and batch learning (or "offline" learning) techniques, is that in online learning the mapping is updated after the arrival of every new datapoint in a scalable fashion, whereas batch techniques are used when one has access to the entire training dataset at once. Online learning could be used in the case of a process occurring in time, for example the value of a stock given its history and other external factors, in which case the mapping updates as time goes on and we get more and more samples. Ideally in online learning, the memory needed to store the function remains constant even with added datapoints, since the solution computed at one step is updated when a new datapoint becomes available, after which that datapoint can then be discarded. , where
Pearltrees Radically Redesigns Its Online Curation Service To Reach A Wider Audience Pearltrees, the Paris-based online curation service that launched in late 2009, was always known for its rather quirky Flash-based interface that allowed you to organize web bookmarks, photos, text snippets and documents into a mindmap-like structure. For users who got that metaphor, it was a very powerful service, but its interface also presented a barrier to entry for new users. Today, the company is launching a radical redesign that does away with most of the old baggage of Pearltrees 1.0. Gone are the Flash dependency, the tree diagrams, the little round pearls that represented your content and most everything else from the old interface. Here is what Pearltrees 1.0 looked like:
Magic Leap Google Investment Google has led a $542 million investment in Magic Leap, a technology startup based in Florida, the company announced Tuesday morning. Magic Leap is a stealth company that describes itself as being a "developer of novel human computing interfaces and software." It just closed a $50 million-plus Series A round in February. The company is working on a new kind of augmented reality — which it calls cinematic reality — that it believes will provide a more realistic 3D experience than anything else that's out there today.
History of the Perceptron History of the Perceptron The evolution of the artificial neuron has progressed through several stages. The roots of which, are firmly grounded within neurological work done primarily by Santiago Ramon y Cajal and Sir Charles Scott Sherrington . Ramon y Cajal was a prominent figure in the exploration of the structure of nervous tissue and showed that, despite their ability to communicate with each other, neurons were physically separated from other neurons. With a greater understanding of the basic elements of the brain, efforts were made to describe how these basic neurons could result in overt behaviors, to which William James was a prominent theoretical contributor. Working from the beginnings of neuroscience, Warren McCulloch and Walter Pitts in their 1943 paper, "A Logical Calculus of Ideas Immanent in Nervous Activity," contended that neurons with a binary threshold activation function were analogous to first order logic sentences.
Why Tech Companies Are So Secretive About Self-Driving Cars Self-driving cars occupy the cultural space once dominated by flying cars. Both are a kind of shorthand for “the future.” But while flying cars have become a symbol of a technological promise left unrealized, driverless cars are widely believed to be inevitable in the coming decades. Leading tech companies say that bringing a fully autonomous car to the market is, in the words of the Tesla CEO Elon Musk, “a super high priority,” but it’s hard to know from the outside what most businesses are actually doing to get there.