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Deep learning

Deep learning
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. In a simple case, there might be two sets of neurons: ones that receive an input signal and ones that send an output signal. When the input layer receives an input it passes on a modified version of the input to the next layer. In a deep network, there are many layers between the input and output (and the layers are not made of neurons but it can help to think of it that way), allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear transformations.[1][2][3][4][5][6][7][8][9] Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Deep learning has been characterized as a buzzword, or a rebranding of neural networks.[13][14] Introduction[edit]

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

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Where are the Deep Learning Courses? This is a guest post by John Kaufhold. Dr. Kaufhold is a data scientist and managing partner of Deep Learning Analytics, a data science company based in Arlington, VA. Education, post-structuralism and the rise of the machines I was asked by the excellent Sheryl Nussbaum-Beach to speak to her PLP class about MOOCs, and, while we had what i thought was an excellent forty minute chat, there were tons of comments that i never had the chance to address. As i look over the questions they asked, I see that in answering their questions i have a chance to lay out many of the thoughts that I have had about MOOCs while they have been all the rage here on the internet in the last few weeks. I opened the discussion with a quick personal intro to my contribution to the MOOC discussion and then we moved to Q & A.

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]

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Quoc Le’s Lectures on Deep Learning Dr. Quoc Le from the Google Brain project team (yes, the one that made headlines for creating a cat recognizer) presented a series of lectures at the Machine Learning Summer School (MLSS ’14) in Pittsburgh this week. This is my favorite lecture series from the event till now and I was glad to be able to attend them. The good news is that the organizers have made available the entire set of video lectures in 4K for you to watch. But since Dr.

Deep learning from the bottom up This document was started by Roger Grosse, but as an experiment we have made it publicly editable. (You need to be logged in to edit.) In applied machine learning, one of the most thankless and time consuming tasks is coming up with good features which capture relevant structure in the data. Deep learning is a new and exciting subfield of machine learning which attempts to sidestep the whole feature design process, instead learning complex predictors directly from the data. 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?

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.

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."

Neural networks and deep learning The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. Deep Learning Schedule Overview Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including visual object or pattern recognition, speech perception, and language understanding. 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.

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.

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:

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