what are bayesian neural networks?
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A Bayesian neural network (BNN) refers to extending standard networks with posterior inference. Standard NN training via optimization is (from a probabilistic perspective) equivalent to maximum likelihood estimation (MLE) for the weights. ... Using MLE ignores any uncertainty that we may have in the proper weight values.
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However, RNNs suffer from the problem of vanishing gradients, which hampers learning of long data sequences. The gradients carry information used in the RNN parameter update and when the gradient becomes smaller and smaller, the parameter updates become insignificant which means no real learning is done.
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From Wikipedia, the free encyclopedia. A Hopfield network is a form of recurrent artificial neural network popularized by John Hopfield in 1982, but described earlier by Little in 1974. Hopfield networks serve as content-addressable ("associative") memory systems with binary threshold nodes.
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A neural network is a black box in the sense that while it can approximate any function, studying its structure won't give you any insights on the structure of the function being approximated.
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Classification models predict categorical class labels; and prediction models predict continuous valued functions.
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The example is using MNIST, but it can be applied to any image. However, I'll point out that you're unlikely to find many examples of using an RNN to classify an image because RNNs are inferior to CNNs for most image processing tasks.
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KNN is called memory-based or lazy learning because the way it learns is just storing the representations of the training examples. An object is classsifed based on the majority votes of its neighbors (the training set). The new example object will be assigned to the class with its most similar k nearest neighbors.
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Fully Convolutional Networks (FCNs) owe their name to their architecture, which is built only from locally connected layers, such as convolution, pooling and upsampling. Note that no dense layer is used in this kind of architecture. This reduces the number of parameters and computation time.
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Naive Bayes is a linear classifier Naive Bayes leads to a linear decision boundary in many common cases. Illustrated here is the case where P(xα|y) is Gaussian and where σα,c is identical for all c (but can differ across dimensions α). The boundary of the ellipsoids indicate regions of equal probabilities P(x|y).
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Deep-belief networks are used to recognize, cluster and generate images, video sequences and motion-capture data. A continuous deep-belief network is simply an extension of a deep-belief network that accepts a continuum of decimals, rather than binary data.
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Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features: ... Provides several learning algorithms; makes creating new ones simple. Supports hard and soft structural priors.
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A Naive Bayes classifier is a probabilistic machine learning model that's used for classification task. The crux of the classifier is based on the Bayes theorem.
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There isn't really a consensus on the definition of a shallow neural network but a neural network with one hidden layer is considered a shallow neural network whereas a network with many hidden layers and a large number of neurons in each layer is considered a deep neural network.
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The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network. The NARX model is based on the linear ARX model, which is commonly used in time-series modeling. The defining equation for the NARX model is.
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The posterior probability is calculated by updating the prior probability using Bayes' theorem. In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred.
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In General, A Discriminative model ‌models the decision boundary between the classes. A Generative Model ‌explicitly models the actual distribution of each class. In final both of them is predicting the conditional probability P(Animal | Features). ... A Generative Model ‌learns the joint probability distribution p(x,y).
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FNN: Fast Nearest Neighbor Search Algorithms and Applications. Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications including KNN classification, regression and information measures are implemented. Version: 1.1.3.