A **neural** network is data processing system which consists of large number of simple & highly interconnected elements which are used for processing in a framework which is inspired by the archi. of cerebral cortex portion of brain. On other hand, **neural** **networks** are generally capable of doing tasks which humans or animals do very well but which are not done by the conventional computers. **Neural** **networks** emerged in the past few years as area of unusual opportunity for research, application & development to variety of real world issues which arise.

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This seminar is about the artificial **neural** network application in processing industry. An artificial **neural** network as a computing system is made up of a number of simple and highly interconnected processing elements, which processes information by its dynamic state response to external inputs. In recent times study of ANN models have gained rapid and increasing importance because of their potential to offer solutions to some of the problems in the area of computer science and artificial intelligence. Instead of performing a program of instructions sequentially, **neural** network models explore many competing hypothesis simultaneously using parallel nets composed of many computational elements. No assumptions will be made because no functional relationship will be established. Computational elements in **neural** **networks** are non linear models and also faster. Hence the result comes out through non linearity due to which the result is very accurate than other methods. The algorithms are presented its clearly illustrator how multi layer **neural** network identifies the system using forward and

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The activation n of neuron N is given by some function of its net input, n = f (n_in). For example: The logistic sigmoid function (an S shaped curve) f (m) = 1/1+exp (-m). **Neural** Network learn by examples, they can‟t programmed to perform a specific tasks as the examples must be selected carefully otherwise the time used is waste or the network might not function properly. Network finds its solution itself its operation can be unpredictable. **Neural** **Networks** and conventional algorithmic computers are not in competition but complement each other.

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Interpretation methods for **neural** **networks** Interpre- tation of **neural** network predictions is an active research area. Post-hoc interpretability (Lipton 2016) is one family of methods that seek to “explain” the prediction without considering the details of black-box model’s hidden mech- anisms. These include methods to explain predictions in terms of the features of the test example, as well as in terms of the contribution of training examples to the test time pre- diction. These interpretations have gained increasing popu- larity, as they confer a degree of insight to human users of what the **neural** network might be doing (Lipton 2016). We describe several widely-used interpretation methods in what follows.

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Artificial **neural** **networks** are mathematical models of simulated neurons based on our present understanding of the biological nervous system. The characteristics of the well-studied models, for example the Backpropagation model, ART, the Perceptron, Self-Organising Maps are well documented [Lippma87, Wasser89, Dayhof90, HerKro91]. Typically, a **neural** network like that of a Backpropagation model is composed of a number of processing elements (nodes) that are densely interconnected by links with variable weights. Unlike conventional sequential processing, all the adjacent nodes process their outputs in parallel. Each node delivers an output, y according to an activation rule. In its simplest form, the rule for a non-linear output of a node is a sum of its N weighted inputs as shown in Figure 1.1. The transition function,/, normally has a binary, linear, sigmoid or hyperbolic tangent characteristic. As a result, a **neural** model is made unique by the specifications of the topology and dimension of the network, the characteristics of the nodes including the type of transition function used, and the learning algorithm.

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Let’s define convolutional **neural** **networks** (CNNs, or ConvNets) with the task of Natural Language Inference. Natural Language Inference is the task of predicting given a premise sentence and an hypothesis whether the couple (premise, hypothesis) is an entailment, a contradiction or neutral (there is no evidence of the hypothesis being true or false).

The system provides support for implementing control and monitoring operations that are typically required by **neural** network programming systems. In performing these operations, control packets specifying exclusive virtual links, are exchanged between the host and the processor nodes. Control packets having different purposes, initiate a con trol operation at the processor nodes by means of virtual link numbers that point to specific service routines. Certain control packets can be broadcasted to a group o f nodes but others, either because of the nature of the operation or because o f the volume o f data returned, are restricted to specific nodes. At this stage perhaps the major deficiency of the **Neural**-RISC system architecture in this area is the lack of a global broadcast bus for connecting processors directly to the host. Operations that require saving pertinent data within the network, including those for saving partially trained **neural** **networks** for later processing, are poorly supported by the system architecture. The amount o f data involved make these operations impractical. A global broadcast bus could solve this problem, with a unquestionable benefit for performance. However, it must be noted that, due to elementary physics limits, a single global broadcast bus can not support a large number of processors. Consequently, many buses would be needed to completely link the pro cessors to the host. Such alternative would certainly impair the homogeneity o f the com munication plan and therefore, it was not implemented. At this point, it is worth men tioning that our option for a smoothly extensible, parallel system architecture is largely supported by recent studies performed as part of an IEEE standardisation project for a multiprocessor scalable interface^4,

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In conventional **neural** **networks**, we have to define the architecture prior to training but in constructive **neural** **networks** the network architecture is constructed during the training process. In this paper, we review constructive **neural** network algorithms that constructing feedforward architecture for regression problems. Cascade-Correlation algorithm (CCA) is a well-known and widely used constructive algorithm. Cascade 2 algorithm is a variant of CCA that is found to be more suitable for regression problems and is reviewed in this paper. We review our recently proposed two constructive algorithms that emphasize on architectural adaptation and functional adaptation during training. To achieve functional adaptation, the slope of the sigmoidal function is adapted during learning. The algorithm determines not only the optimum number of hidden layer nodes, as also the optimum value of the slope parameter of sigmoidal function. The role of adaptive sigmoidal activation function has been verified in constructive **neural** **networks** for better generalization performance and lesser training time.

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Singh and Mars (2010) studied the application of support vector machines to forecast changes in CD4 count of HIV-1 positive patients. HIV infection can be effectively managed with antiretroviral (ARV) drugs, but close monitoring of the progression of the disease is vital. One of the better surrogate markers for the disease progression is the use of CD4 cell counts. Forecasting CD4 cell count helps clinicians with treatment management and resource allocation. The aim of this research was to investigate the application of machine learning to predict future CD4 count change. The model took as input the genome, current viral load and number of weeks from baseline CD4 count and predicted the range of CD4 count change. The model produced an accuracy of 83%. Deeb and Jawabreh (2012) presented a quantitative structure-activity relationship study using artificial **neural** network (ANN) methodology to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups. The results obtained by artificial **neural** **networks** gave advanced regression models with good prediction ability. Therefore, artificial **neural** **networks** provided improved models for heterogeneous data sets without splitting them into families.

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This document discusses the derivation and implementation of convolutional **neural** **networks** (CNNs) [3, 4], followed by a few straightforward extensions. Convolutional **neural** **networks** in- volve many more connections than weights; the architecture itself realizes a form of regularization. In addition, a convolutional network automatically provides some degree of translation invariance. This particular kind of **neural** network assumes that we wish to learn filters, in a data-driven fash- ion, as a means to extract features describing the inputs. The derivation we present is specific to two-dimensional data and convolutions, but can be extended without much additional effort to an arbitrary number of dimensions.

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Back propagation may be a common methodology of training artificial **neural** **networks** so as to minimize the objective function...it's a supervised learning methodology, and may be a generalization of the delta rule. It needs a dataset of the required output for several inputs, creating up the training set. It’s most helpful for feedforward **networks**. The term is an abbreviation for "backward propagation of errors". Back propagation needs that the activation function used by the artificial neurons be differentiable... the. Itcan be divided into two phases: propagation and weight update.

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analogies, of course, are not sufficient to justify the treatment of the problem with eigenvalue equations, as happens in the physical systems modeled by the Schr¨ odinger equation, and are used in this paper exclusively as a starting point that deserves further study. However it is a line of research that can clarify intimate aspects of the optimization of an artificial **neural** network and propose a new point of view of this process. We will demonstrate in the following sections that meaningful conclusions can be reached and that the proposed treatment actually allows to optimize artificial **neural** **networks** by applying the formalism to some datasets available in literature. A first thought on the model is that it allows to naturally define the energy of the network, a concept already used in some types of ANNs, such as the Hopfield **networks** in which Lyapunov or energy functions can be derived for binary element **networks** allowing a complete characterization of their dynamics, and permits to generalize the concept of energy for any type of ANN.

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This work attempts to explain the types of computation that **neural** **networks** can perform by relating them to automata. We first define what it means for a real-time network with bounded precision to accept a language. A measure of network memory follows from this definition. We then characterize the classes of languages acceptable by various recurrent net- works, attention, and convolutional **networks**. We find that LSTMs function like counter ma- chines and relate convolutional **networks** to the subregular hierarchy. Overall, this work at- tempts to increase our understanding and abil- ity to interpret **neural** **networks** through the lens of theory. These theoretical insights help explain **neural** computation, as well as the rela- tionship between **neural** **networks** and natural language grammar.

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LRCN [84] is a class of models that is spatially and temporally deep and can be applied to a variety of computer vision tasks. In this paper long term recurrent CNNs are proposed which is a novel architecture for visual recognition and description. Current models like TACoS assume a fixed spatio -temporal receptive field or simple temporal averaging. Recurrent convolutional models are doubly deep. They can be compositional in spatial as well as temporal layers. Such models have advantages when target concepts are complex. This approach compared with the TACoS [83] multilevel approach achieves a performance of 28.8% whereas the TACoS achieve a performance of 26.9%. Recognizing arbitrary multiple digits from Street view imagery is a very challenging task. This difficulty arises due to the wide variability in the visual appearance of text in the wild on account of a large range of fonts, colours, styles, orientations, and character arrangements. Traditional approaches [85] to solve this problem typically separate out the localization, segmentation, and recognition steps. However in this paper, a unified approach is followed using CNN which is directly applied on the image pixels. The DistBelief [86] implementation of **neural** **networks** is used in order to train CNN on high quality images. This approach increases the accuracy of recognizing complete street numbers to 96% and accuracy of recognizing per digit to 97.84%.

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1. Modal Learning **Neural** **Networks** Twenty years ago there were already several forms of artificial **neural** network, each utilising a different form of learning. But at that time it was considered likely that one or at least a very small number of forms of learning would prevail and become ubiquitous. Along the way, many forms of learning, notably Back- propagation, Bayesian and Kernel methods have been hailed as superior forms. However, decades after the introduction of Kohonen learning, SOMs, and Backpropagation they are still being used alongside more recent methods such as Bayesian and SVM. No single method or mode prevails. A wide range of methods are still in use, simply because there are significant problems and datasets for which each method is suitable and effective.

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The convolution layer is the core building block of a con- volutional **neural** network. The convolution layer consists of layers of filters with each of layer looking at a specific feature of the image. For example, one filter might be look- ing for edges while another is looking for curves. A filter is only looking at a small spatial area of the original input but would convolve across the width and height of the input image. The size of this small area is a parameter we can control (hyper-parameter) called the receptive field. This aspect of convolution layers is what makes CNN more op- timized for image classification. A filter is made up of an array of trainable weights, and these weights are the same no matter where in the image the filter is applied as the filter is convolving around the image. In regular **neural** **networks**, a node inside the first hidden layer would have weight corre- sponding to all the elements inside the three 2-dimension ar- rays. For image classification problem, this fully-connected aspect of a regular **neural** network does not scale well to im- ages [1]. The amount of weights needed to be trained would increase exponentially as the image size increases. The de- sign of convolution layers allows for less weights to be trained (only those needed by the relatively small receptive field of the filter) which decreases training time and computational load.

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Unlike SNNs, deep **neural** **networks** (DNNs) have been able to perform the state-of-the-art results on many complex tasks such as image recognition (Krizhevsky, Sutskever, and Hinton 2012; Krizhevsky 2009; Simonyan and Zisserman 2014; He et al. 2015), speech recognition (Abdel-Hamid et al. 2012; Sainath et al. 2013; Hinton et al. 2012), natural lan- guage processing (Kim 2014; Severyn and Moschitti 2015) and so on. But heavy computation load promotes researchers to find more efficient approach to deploy them in mobiles or embedded systems. This inspires the SNN researchers that a fully-trained DNN might be slightly tuned to be directly converted to a SNN without complicated training proce- dure. Beginning with the work of (Perezcarrasco et al. 2013), where DNN units were translated into biologically inspired spiking units with leaks and refractory periods, continu- ous efforts have been made to realize this idea. After a se- ries of success in transferring deep **networks** like Lenet and VGG-16 (Cao, Chen, and Khosla 2015; Diehl et al. 2015; Rueckauer et al. 2017), now the rate-coding based SNN can achieve state-of-the-art performance with minor accu- racy loss even in the conversion of complicated layers like Max-Pool, BatchNorm and SoftMax.

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In this paper, we present a hypergraph **neural** **networks** (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph struc- ture. Confronting the challenges of learning representation for complex data in real practice, we propose to incorpo- rate such data structure in a hypergraph, which is more flexi- ble on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning proce- dure can be conducted using hyperedge convolution opera- tions efficiently. HGNN is able to learn the hidden layer rep- resentation considering the high-order data structure, which is a general framework considering the complex data correla- tions. We have conducted experiments on citation network classification and visual object recognition tasks and com- pared HGNN with graph convolutional **networks** and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the- art methods. We can also reveal from the results that the pro- posed HGNN is superior when dealing with multi-modal data compared with existing methods.

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Our work is inspired by Ruiz and Owens [8] in which they has shown a ring like RNN is able to learn and replicate a particular class of time varying periodic signals, here we present two models of a three node recurrent **neural** **networks**. These models are three nonlinear coupled equations having unique equilibrium point and bounded solution. We have shown that without ring like structure RNN can still generate a limit cycle .