Category Archives: Neural Networks

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Integration of Swarm Intelligence And Artificial Neural

Category : Neural Networks

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Could you get deep learning with a shallower talent pool? If you know other interesting projects or think we missed anything, we would love to hear about it in the comments below. Getting started Setup CNTK on your machine Enabling 1bit SGD Developing and Testing CNTK Production Test Configurations How to contribute to CNTK Tutorial Tutorial II CNTK usage overview Examples Presentations Multiple GPUs and machines Configuring CNTK Config file overview Simple Network Builder BrainScript Network Builder SGD block Reader block Train, Test, Eval Top-level configurations Describing Networks Basic concepts Expressions Defining functions Full Function Reference Data readers Text Format Reader CNTK Text Format Reader UCI Fast Reader (deprecated) HTKMLF Reader LM sequence reader LU sequence reader Image reader Evaluating CNTK Models Overview C++ Evaluation Interface C# Evaluation Interface Evaluating Hidden Layers C# Image Transforms for Evaluation Advanced topics Command line parsing rules Top-level commands Plot command ConvertDBN command DAY 1 - ARTIFICIAL NEURAL NETWORKS Introduction and ANN Structure.
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Advanced Intelligent Computing Theories and Applications:

Category : Neural Networks

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In our set of experiments, we consider several concrete applications: movie recommendation over 11K movies, personalized image summarization with 10K images, and revenue maximization on the YouTube social networks with 5000 communities. It involves children participating in various competitive and cooperative robot challenges. In the stochastic sense, the system can only determine the probability for a unit to take one of the values, -1 or +1.
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Applications and Science of Neural Networks, Fuzzy Systems,

Category : Neural Networks

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Whether you're trying to translate something into a different language, turn your spoken words into text or sift through thousands of saved photos for that one special snapshot, Google has built a "smarter" artificial intelligence system to help, company representatives announced this week. A neural network learns the associations from its input data and it doesn't have enough raw storage to simply remember all the input-output associations. Connectionists presume that cognitive functioning can be explained by collections of units that operate in this way.
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Advanced Research on Electronic Commerce, Web Application,

Category : Neural Networks

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Springer, 1993. [16] Learning where to attend with deep architectures for image tracking. So not only did DNNs drive error rates down at once, but because there is such a huge space of largely untested possibilities under the umbrella of “DNNs,” like different topologies, numbers of layers and nodes, how the nodes are structured, how they are trained etc., they promise a lot of potential for the years to come. They look very similar to LSMs and ESNs, but they are used more like FFNNs.
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Financial Prediction Using Neural Networks

Category : Neural Networks

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The activation functions of the network nodes are Kolmogorov-Gabor polynomials that permit additions and multiplications. Chichester; New York: Wiley. [ Cited by 55 ] This paper discusses a buying and selling timing prediction system for stocks on the Tokyo Stock Exchange and analysis of internal representation. Varying numbers of layers and layer sizes can be used to provide different amounts of abstraction. [4] Deep learning exploits this idea of hierarchical explanatory factors where higher level, more abstract concepts are learned from the lower level ones.
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Multiple Classifier Systems: 9th International Workshop, MCS

Category : Neural Networks

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Ah and i forgot: this link is a very nice forum where you can look at problems other people have and learn from that. In practice by the way (and we will get to this once again later), all Neural Network libraries always compute the analytic gradient, but the correctness of the implementation is verified by comparing it to the numerical gradient. In March 2012, they won the contest on all three evaluation metrics by a large margin, with superhuman performance in terms of pixel error. (Ranks 2–6: for researchers at ETHZ, MIT, CMU, Harvard.) A NIPS 2012 paper on this is coming up.
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Adaptive Neural Network Control of Robot (World Scientific

Category : Neural Networks

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Various groups have attempted to automate the process of epilepsy diagnosis using pattern recognition programs to spot the characteristic signature of the condition in EEG data. DeepMind has its own take on deep learning, based on a closer study of the brain itself. Whoever runs the network defines what the “correct” final output should be. For example, let’s consider an analyst who works with confidential customer data.
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Gateway to Memory: An Introduction to Neural Network

Category : Neural Networks

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MRS bears similarities with information-theoretic approaches such as entropy search (ES). Abstract We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. As a result of this view, research on connectionist networks for applications in artificial intelligence was dramatically reduced in the 1970's (McClelland and Rumelhart, 1988; Joshi et al., 1997). The proposed subspace learning based approach recovers the LR component using only a small subset of the columns/rows of data and reduces complexity to $O(\max(N_1,N_2) r^2)$.
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Neural Networks in Business: Techniques and Applications

Category : Neural Networks

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Which one can hear “nose” in an input image, and know that should be labeled as a face and not a frying pan? Three or more layers of artificial neurons are used with one layer representing input data and one layer representing the corresponding output. Deep learning is already working in Google search, and in image search; it allows you to image search a term like “hug.” It’s used to getting you Smart Replies to your Gmail, it’s in speech and vision, it will soon be used in machine translation I believe.
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Advances in Neural Information Processing Systems 10:

Category : Neural Networks

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Also note that when implementing this forward-propagation step, we should keep track of the feed-forward pre-activations, as these will be used for calculating backpropagated errors and error function gradients. Each block estimates the same final label class y, and its estimate is concatenated with original input X to form the expanded input for the next block. Later, it continues to "learn" about other aspects of the data which may be spurious from a general viewpoint.
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