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The project describes teaching process of multi-layer neural network employing backpropagation algorithm. To illustrate this process the three layer neural network with two inputs and one output,which is shown in the picture below, is used: Each neuron is composed of two units. To teach the neural network we need training data set. Propagation of signals through the hidden layer. Propagation of signals through the output layer. In the next algorithm step the output signal of the network y is compared with the desired output value (the target), which is found in training data set. It is impossible to compute error signal for internal neurons directly, because output values of these neurons are unknown. The weights' coefficients wmn used to propagate errors back are equal to this used during computing output value. When the error signal for each neuron is computed, the weights coefficients of each neuron input node may be modified. Coefficient h affects network teaching speed. Related:  Artificial Neural NetworkDeep Learningcognitive modelling

Hopfield Neural Network Hopfield Network Simulation Hopfield Network is an example of the network with feedback (so-called recurrent network), where outputs of neurons are connected to input of every neuron by means of the appropriate weights. In the event of the net that work as autoassociative memory (our case) weights which connect neuron output with its input are zeros and the matrix of weights W is symmetrical. The activation function of a single neuron looks as follows: where i and j means index of neurons in N-neural Hopfield Network, and k is a time moment. In the recovery mode weights of netowrk connections are constant. In the training mode the weights are calculated on the base of the teaching (pattern) vectors. where k means index of teaching vector and K the number of all teaching vectors. Description of the simulation: In our example we recognize 9-pixel picture with the 9-neuron neural network. and the third one is clear picture which is the vector that consists only -1. Provider: MSc A.Gołda June 2005

Learning Vector Quantization (LVQ) Next: Support Vector Machines (SVM) Up: Competitive Learning Networks Previous: Self-organizing property of the Learning Vector Quantization (LVQ) LVQ is a supervised learning algorithm based on a set of training vectors with known classes (labeled). However, if the winning node does not belong to the class of the input vector, its weight vector is moved away from the input vector: The weight vectors of all other nodes are unchanged. This LVQ algorithm can be improved if two nodes closest to the input vector are considered. Basic Neural Network Tutorial – Theory | Taking Initiative Well this tutorial has been a long time coming. Neural Networks (NNs) are something that i’m interested in and also a technique that gets mentioned a lot in movies and by pseudo-geeks when referring to AI in general. They are made out to be these really intense and complicated systems when in fact they are nothing more than a simple input output machine (well at least for the standard Feed Forward Neural Networks (FFNN) ). As with any field the more you delve into it the more technical it gets and NNs are the same, the more research you do into them the more complicated architectures, training techniques, activation functions become. For now this is just a simple primer into NNs. Introduction to Neural Networks There are many different types of neural networks and techniques for training them but I’m just going to focus on the most basic one of them all – the classic back propagation neural network (BPN). This BPN uses the gradient descent learning method. The Neuron Simple, huh?

Autoassociative memory Autoassociative memory, also known as auto-association memory or an autoassociation network, is often misunderstood to be only a form of backpropagation or other neural networks. It is actually a more generic term that refers to all memories that enable one to retrieve a piece of data from only a tiny sample of itself. Traditional memory stores data at a unique address and can recall the data upon presentation of the complete unique address. Autoassociative memories are capable of retrieving a piece of data upon presentation of only partial information from that piece of data. Heteroassociative memories, on the other hand, can recall an associated piece of datum from one category upon presentation of data from another category. For example, the fragments presented below should be all that's necessary to retrieve the appropriate memory: "A day that will live in ______""To be or not to be""I came, I saw, I conquered" See also[edit] Bidirectional Associative Memory References[edit]

Simple Recurrent Network Since the publication of the original pdp books (Rumelhart et al., 1986; McClelland et al., 1986) and back-propagation algorithm, the bp framework has been developed extensively. Two of the extensions that have attracted the most attention among those interested in modeling cognition have been the Simple Recurrent Network (SRN) and the recurrent back-propagation (RBP) network. In this and the next chapter, we consider the cognitive science and cognitive neuroscience issues that have motivated each of these models, and discuss how to run them within the PDPTool framework. 7.1.1 The Simple Recurrent Network The Simple Recurrent Network (SRN) was conceived and first used by Jeff Elman, and was first published in a paper entitled Finding structure in time (Elman, 1990). Figure 7.1: The SRN network architecture. An SRN of the kind Elman employed is illustrated in Figure 7.1. The beauty of the SRN is its simplicity. Here we briefly discuss three of the findings from Elman (1990).

Introduction to Neural Networks Instructors: Nici Schraudolph and Fred CumminsIstituto Dalle Molle di Studi sull'Intelligenza Artificiale Lugano, CH [Content][Organization][Contact][Links] Course content Summary Our goal is to introduce students to a powerful class of model, the Neural Network. In fact, this is a broad term which includes many diverse models and approaches. We then introduce one kind of network in detail: the feedforward network trained by backpropagation of error. Lecture 1: Introduction Lecture 2: The Backprop Toolbox By popular demand: Lectures 1 & 2 as a ZIP file. Lecture 3: Advanced Topics By popular demand, a list of English terms for mathematical expressions that we are using. A few suggestions for possible project topics. Lecture 3 as a ZIP file. [Top] Organization This module will consist of three extended lectures of three hours each. Contact The best way to reach us is either at the lectures (during the break, or after the end) or by email: Links Related stuff of interest: A page of neural network links

Learning rule Learning rule or Learning process is a method or a mathematical logic which improves the neural network's performance and usually this rule is applied repeatedly over the network. It is done by updating the weights and bias levels of a network when a network is simulated in a specific data environment.[1] A learning rule may accept existing condition ( weights and bias ) of the network and will compare the expected result and actual result of the network to give new and improved values for weights and bias. [2] Depending on the complexity of actual model, which is being simulated, the learning rule of the network can be as simple as an XOR gate or Mean Squared Error or it can be the result of multiple differential equations. The learning rule is one of the factors which decides how fast or how accurate the artificial network can be developed. Depending upon the process to develop the network there are three main models of machine learning: See also[edit] References[edit]

UFLDL Tutorial - Ufldl From Ufldl Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems. This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent). Sparse Autoencoder Vectorized implementation Preprocessing: PCA and Whitening Softmax Regression Self-Taught Learning and Unsupervised Feature Learning Building Deep Networks for Classification Linear Decoders with Autoencoders Working with Large Images Note: The sections above this line are stable. Miscellaneous Miscellaneous Topics Advanced Topics: Sparse Coding ICA Style Models Others Material contributed by: Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen