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Deep learning

Deep learning
Branch of machine learning Deep learning (also known as deep structured learning or differential programming) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.[1][2][3] Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.[4][5][6] Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. Definition[edit] Overview[edit] History[edit]

https://en.wikipedia.org/wiki/Deep_learning

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Recurrent 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.

Rhizomatic Learning - The community is the curriculum Doing this course I've put together a blog post to give you a sense of 'where' the course is happening and what you might like to do as part of it. READ THIS FIRST = Your unguided tour of Rhizo14 Why might this course be for you? Services of Internet - WWW, E-mail, News, FTP Cookies are used only to analyse traffic and provide advertising at the Website.More about it here. Internet service providers (ISP - Internet Service Provider) companies or institutions (such as T-Com, Iskon or CARNet in Croatia, AT&T in US and MTNL in India), which satellite or optical connections with several major Internet node abroad (mainly in the direction of America and Europe) and the thus ensuring high capacity connection to the rest of the Internet world. However, practice has shown that it can barely follow the needs of the growing number of members of Internet communities. When selecting an ISP of significance is the number of services that it provides to its customers.

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.

20 Resources for Teaching Kids How to Program & Code Isn't it amazing to see a baby or a toddler handle a tablet or a smart phone? They know how technology works. Kids absorb information so fast, languages (spoken or coded) can be learned in a matter of months. Recently there has been a surge of articles and studies emerging about teaching kids to code. 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:

Towards Reproducible Descriptions of Neuronal Network Models Introduction Science advances human knowledge through learned discourse based on mutual criticism of ideas and observations. This discourse depends on the unambiguous specification of hypotheses and experimental procedures—otherwise any criticism could be diverted easily. Moreover, communication among scientists will be effective only if a publication evokes in a reader the same ideas as the author had in mind upon writing [1]. Scientific disciplines have over time developed a range of abstract notations, specific terminologies and common practices for describing methods and results.

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