###### Convolutional Neural Networks

- CS231n: Convolutional Neural Networks for Visual Recognition at Stanford ( archived 2015 version ) is an amazing advanced course, taught by Fei-Fei Li and Andrej Karpathy (a UofT alum). (toronto.edu)
- ImageNet) and the revival of deep convolutional neural networks (CNN). (pubmedcentralcanada.ca)
- In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. (pubmedcentralcanada.ca)
- In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN) which are one of the recent techniques adopted from the deep learning community. (mdpi.com)
- Different architectures of deep learning have been introduced in recent years, such as deep convolutional neural networks, deep belief networks, and recurrent neural networks. (mdpi.com)

###### Artificial Neural

- The central objective of this project is demonstrating the capabilities of Artificial Neural Network implementations in recognizing extended sets of optical language symbols. (codeproject.com)
- An emerging technique in this particular application area is the use of Artificial Neural Network implementations with networks employing specific guides (learning rules) to update the links (weights) between their nodes. (codeproject.com)
- The evolving component of the framework is based on an Artificial Neural Network with error backpropagation. (springer.com)
- In recent times, the introduction of complex-valued neural networks (CVNNs) has widened the scope and applications of artificial neural network (ANN) [ 25 - 34 ]. (hindawi.com)

###### back propagation

- In 1970-ies, the area got another boom, when the idea of multi-layer neural networks with the back propagation learning algorithm was presented. (codeproject.com)
- The library implements several popular neural network architectures and their training algorithms, like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning. (codeproject.com)
- Part 2 : This one, is about multi layer neural networks, and the back propagation training method to solve a non linear classification problem such as the logic of an XOR logic gate. (codeproject.com)
- After some research I have chosen a multilayer perceptron and standard back-propagation algorithm for training. (codeproject.com)

###### algorithms

- trained a radial basis function neural network with BP and particle swarm optimization (PSO) algorithms for identification of a power system. (hindawi.com)
- In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. (frontiersin.org)
- Part 3: This one is about how to use a genetic algorithm (GA) to train a multi-layer neural network to solve some logic problem, ;f you have never come across genetic algorithms, perhaps my other article located here may be a good place to start to learn the basics. (codeproject.com)

###### ANNs

- Artificial neural networks (ANNs) are a vital component of artificial intelligence. (hindawi.com)
- They partitioned the ANNs into two parts: the output layer versus the other layers and then they trained these two parts with different techniques. (hindawi.com)
- The aim of this study was to investigate the capacity of Artificial Neural Networks (ANNs) to classify and estimate the quality of 27 different pearls from their UV-Visible spectra. (mdpi.com)
- This method is based on artificial neural networks (ANNs), specifically on multilayer perceptrons (MLPs). (springer.com)

###### Architectures

- From that time, many different researchers have studied the area of neural networks, what lead to a vast range of different neural architectures, which were applied to a great range of different problems. (codeproject.com)
- In some cases, it is arguable what is better, because there may be such unusual neural network architectures, where it is hard to split the network into layers and neurons. (codeproject.com)
- But in most cases, it is favorable to split all these entities into distinct classes, what leads not only to easier understanding, but also allows reusing of all these components and building new neural networks architectures from smaller generic pieces. (codeproject.com)
- Our results demonstrate the utility of neural network architectures for the classification of sleep. (frontiersin.org)

###### classification

- This approach we extracted 80 features of every image using ridge scheme of minutia method of feature extraction and multilayer neural network for the classification and recognition of finger print. (scribd.com)
- Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. (hindawi.com)
- The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. (hindawi.com)
- Will be about multi layer neural networks, and the back propogation training method to solve a non-linear classification problem such as the logic of an XOR logic gate. (codeproject.com)
- As a result of our experiments, the BM25-tf-rf scheme and the proposed neural network model is shown to be the best performing classification system for our case study of cancer type classification. (springer.com)

###### Perceptrons

- Multilayer Perceptrons are often used to model complex relationships between sets of data. (hindawi.com)

###### network's inputs

- In [ 8 ] it is described that this can be achieved by analyzing the common interference of the network outputs, which is caused by distortions in the data that is passed to the neural network's inputs. (hindawi.com)

###### 2016

- Orii H., Kawano H., Suetake N., Maeda H. (2016) Color Conversion for Color Blindness Employing Multilayer Neural Network with Perceptual Model. (springer.com)

###### Abstract

- This is a base abstract class, which encapsulates common functionality for all neuron's layers. (codeproject.com)

###### Neurons

- Some other libraries and applications do not extract such entities, like neurons, layers of neurons, or a network of layers, but implement the entire neuron network architecture in a single class. (codeproject.com)
- The approach of InterCriteria Analysis (ICA) was applied for the aim of reducing the set of variables on the input of a neural network, taking into account the fact that their large number increases the number of neurons in the network, thus making them unusable for hardware implementation. (hindawi.com)
- for example, the number of neurons in the perception of the individual values can be too large, and since a proportionally larger amount of memory and computing power is necessary to train the networks, this would lead to a longer periods for training. (hindawi.com)
- When working with neural networks it is essential to reduce the amount of neurons in the hidden layer, which also reduces the number of weight coefficients of the neural network as a whole. (hindawi.com)
- The deep learning techniques are basically composed of multiple hidden layers, and each hidden layer consists of multiple neurons, which compute the suitable weights for the deep network. (mdpi.com)

###### recurrent

- Exponential convergence of a gradient descent algorithm for a class of recurrent neural networks. (berkeley.edu)

###### Backpropagation

- Backpropagation is the most applied such method-in it neural networks are trained with uplink (applied on a Multilayer Perceptron). (hindawi.com)

###### algorithm

- In this paper, we propose a novel digital image color conversion algorithm for color blindness using a multilayer neural network. (springer.com)
- Artificial neural networks (ANN) were applied to construct a relationship between retinal venous pulsation amplitude, IOP (input) and CSFp (output) and develop an algorithm to estimate CSFp based on these parameters. (edu.au)
- In this paper, we propose a novel multimean particle swarm optimization algorithm for multilayer feed-forward artificial neural networks training. (hindawi.com)
- The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feed-forward artificial neural networks training. (hindawi.com)
- Some neural networks libraries tend to combine the entity of neuron's network together with the learning algorithm, what makes it hard to develop another learning algorithm which can be applied to the same neural network architecture. (codeproject.com)
- Genetic Algorithm for Multi-Layer Networks. (informit.com)

###### Classifier

- Another approach is to use a wavelet-based neural network classifier to reduce the power interference in the training of the neural network or randomly stumbled measurements [ 5 ]. (hindawi.com)
- Here the discrete wavelet transform (DWT) technique is integrated with the neural network to build a classifier. (hindawi.com)

###### coefficients

- The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. (mdpi.com)
- A new approach for determining the coefficients of a complex-valued autoregressive (CAR) and complex-valued autoregressive moving average (CARMA) model coefficients using complex-valued neural network (CVNN) technique is discussed in this paper. (hindawi.com)
- The CAR and complex-valued moving average (CMA) coefficients which constitute a CARMA model are computed simultaneously from the adaptive weights and coefficients of the linear activation functions in a two-layered CVNN. (hindawi.com)
- The results show that the proposed method can accurately determine the model coefficients provided that the network is properly trained. (hindawi.com)

###### 1994

- In Proceedings of the Fifth Australian Conference on Neural Networks , pages 198--201, 1994. (berkeley.edu)

###### Introduction

- This course serves as an introduction to machine learning, with an emphasis on neural networks. (toronto.edu)
- Is an introduction into Perceptron networks (single layer neural networks). (codeproject.com)
- AI: An introduction into neural networks (multi-layer networks / trained by Microbial GA). (codeproject.com)

###### Fuzzy

- Now I am interested about AI (Neural Network, Fuzzy Logic and GA). Currently based in Vladivostok, Russia. (codeproject.com)
- they are presented in sections on theoretical aspects of fuzzy reasoning and fuzzy control, fuzzy neural networks, fuzzy control applications, fuzzy logic planning, and fuzzy circuits. (springer.com)

###### implement

- In this paper, we implement finger print recognition scheme with multilayer neural network and minutia method for the feature extraction. (scribd.com)
- As I a little know neural network I tried to implement such feature themselves. (codeproject.com)

###### inputs

- So to work out the activation for an output node we need to make use of the output from the hidden layer nodes, which are used as inputs to the output layer nodes. (codeproject.com)
- In Reinforcement learning, during training, a set of inputs is presented to the Neural Network, the Output is 0.75, when the target was expecting 1.0. (codeproject.com)
- The sample complexity of neural network learning with discrete inputs. (berkeley.edu)

###### adaptive

- By passing through the atmosphere a series of turbulent layers modify the light's wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. (mdpi.com)
- There are two major tomographic AO techniques currently under development: Multi Conjugate (MCAO) [ 2 , 3 ] and Multi Object Adaptive Optics (MOAO) [ 4 , 5 ]. (mdpi.com)
- An adaptive multi-tiered framework, that can be utilised for designing a context-aware cyber physical system to carry out smart data acquisition and processing, while minimising the amount of necessary human intervention is proposed and applied. (springer.com)

###### CodeProject

- The article is not intended to provide the entire theory of neural networks, which can be found easily on the great range of different resources all over the Internet, and on CodeProject as well. (codeproject.com)

###### computation

- Neural Computation , 5(3):371--373, 1993. (berkeley.edu)
- Neural Computation , 7:990--1002, 1995. (berkeley.edu)

###### framework

- In proposed framework, a neural network is learning about a relationship of an image data and a discrimination performance of colors in an image, and a color conversion rule is trained as a part of a neural network. (springer.com)
- Leveraging deep neural network on the TensorFlow framework, we developed a computational tool, integrated Mental-disorder GEnome Score (iMEGES), for analyzing whole genome/exome sequencing data on personal genomes. (springer.com)

###### Proceedings

- In Proceedings of the Fourth Australian Conference on Neural Networks , pages 14--17, 1993. (berkeley.edu)
- In Proceedings of the Fourth Australian Conference on Neural Networks , pages 161--164, 1993. (berkeley.edu)
- In Proceedings of the Sixth Australian Conference on Neural Networks , pages 201--204, 1995. (berkeley.edu)
- In Proceedings of the Sixth Australian Conference on Neural Networks , pages 189--192, 1995. (berkeley.edu)

###### method

- In the proposed method, we design a multilayer neural network composed of three building blocks: layers for image color conversion, layers for perceptual model of color blindness, and layers for color discrimination. (springer.com)
- A method of training a neural network (2) having dynamically adjustable parameters controlled by a controller (10) which determine the response of the network (2). (google.com.au)
- Here, for the first time, with the help of the ICA method, correlations between triples of the input parameters for training of the neural networks were obtained. (hindawi.com)
- In [ 6 ] a method is presented for parameter metaoptimization based on PSO and it is applied to neural network training. (hindawi.com)

###### methods

- Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. (frontiersin.org)
- Therefore, researchers are forced to look for better methods for training neural networks. (hindawi.com)
- There are, however, many other methods that accelerate the training of neural networks [ 1 - 3 ], by reducing memory usage, which in turn lowers the needed amount of computing power. (hindawi.com)

###### data

- The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. (mdpi.com)
- Measuring the similarity of the whole of the output set and target set and adjusting the parameters on this global measure rather than on the similarity of pairs of individual vectors provides enhanced training rates for neural networks having a data throughput rate that can be higher than the rate at which the parameters can be adjusted. (google.com.au)
- CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. (pubmedcentralcanada.ca)
- In this case, we use the approach of ICA for data preprocessing, which may yield reduction of the total time for training the neural networks, hence, the time for the network's processing of data and images. (hindawi.com)
- In the stage of preprocessing, the data at the input of the neural network can be used as a constant threshold value to distinguish static from dynamic activities, as it was done in [ 4 ]. (hindawi.com)
- The removal of nonessential components of the data can lead to smaller sizes of the neural networks, and, respectively, to lower requirements for the input data. (hindawi.com)
- Such networks can be fed the data from the graphic analysis of the input picture and trained to output characters in one or another form. (codeproject.com)
- The main problem was in the representation of an input data for neural network. (codeproject.com)
- We divided our data into 4 sets of 300 training and 100 test vectors and trained a neural net to estimate the gaze direction given the stance vector. (pubmedcentralcanada.ca)

###### recognition

- It's easy now to modify this code to add as many layers as you want (some of the state-of-the-art image recognition models are hundred+ layers of convolutions, max pooling, dropout and etc). (medium.com)
- Matlab Project 1: GMMs and RBF Networks for Speech Pattern Recognition. (informit.com)
- Before testing the recognition ability you must train the network (or you can load an file image of trained net). (codeproject.com)
- A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. (mdpi.com)

###### parameters

- The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. (pubmedcentralcanada.ca)
- We consider dynamic configuration of wireless sensor networks, where certain functions can be automatically assigned to nodes at any time of network operations, based on the parameters such as remaining energy and topology changes. (archive.org)

###### hidden units

- This will create 3 layers of fully connected units with 10, 20 and 10 hidden units respectively, with default Rectified linear unit activations. (medium.com)
- Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets , by Gorman and Sejnowski, Neural Networks , Vol. 1, pgs. (utk.edu)

###### vectors

- A set of input vectors (Il to In) are input to network (2) at an input port (4). (google.com.au)
- The corresponding set of output vectors (O' l to O' n ) provided by the network (2) are compared to a target set of output vectors (O l to O n ) by an error logger (12) which provides to the controller (10) a measure of similarity of the two sets. (google.com.au)

###### model

- In this part let's go deeper and try multi-layer fully connected neural networks, writing your custom model to plug into the Scikit Flow and top it with trying out convolutional networks. (medium.com)
- This model is very similar to the previous one, but we changed the activation function from a rectified linear unit to a hyperbolic tangent (rectified linear unit and hyperbolic tangent are most popular activation functions for neural networks). (medium.com)
- We've created conv_model function, that given tensor X and y, runs 2D convolutional layer with the most simple max pooling - just maximum. (medium.com)
- One such network with supervised learning rule is the Multi-Layer Perceptron (MLP) model. (codeproject.com)
- The very nature of this particular model is that it will force the output to one of nearby values if a variation of input is fed to the network that it is not trained for, thus solving the proximity issue. (codeproject.com)

###### threshold

- Lower bounds on the Vapnik-Chervonenkis dimension of multi-layer threshold networks. (berkeley.edu)

###### distortions

- By passing through the atmosphere, it suffers some distortions caused by a series of optically turbulent layers present at different altitudes and with different relative strengths. (mdpi.com)

###### suitable

- IMHO, the neural network most suitable for this purpose. (codeproject.com)

###### theory

- I think I can point any person concerned to theory directly to several neural network sites. (codeproject.com)

###### applications

- Mangerial applications of neural networks: The Case of Bank Failure Predictions , Management Sceince , Vol. 38, No. 7, July 1992, pgs. (utk.edu)

###### functionality

- Remember with a single layer (perceptron) we can't actually achieve the XOR functionality, as it is not linearly separable. (codeproject.com)

###### demonstrate

- For many such problems, neural networks can be applied, which demonstrate rather good results in a great range of them. (codeproject.com)

###### outputs

- Specifically some network models use a set of desired outputs to compare with the output and compute an error to make use of in adjusting their weights. (codeproject.com)

###### design

- Instead of combining several neural network entities into a single class and making a mess, which leads to loosing flexibility and clarity in the code and design, all entities were split into distinct classes, making them easer to understand and reuse. (codeproject.com)

###### feed-forward

- A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. (hindawi.com)

###### systems

- Modeling systems and functions using neural network mechanisms is a relatively new and developing science in computer technologies. (codeproject.com)

###### output

- As already explained above, there is one input layer, one hidden layer and one output layer. (codeproject.com)
- The output layer, however, knows nothing about the input layer as it is not directly connected to it. (codeproject.com)
- The transfer function of hidden neurones was of tangent sigmoidal type, while the transfer function in the output layer was linear. (thefreedictionary.com)

###### favorable

- The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN. (mdpi.com)

###### input

- This is easily achieved for the hidden layer as it has direct links to the actual input layer. (codeproject.com)
- The technical approach followed in processing input images, detecting graphic symbols, analyzing and mapping the symbols and training the network for a set of desired Unicode characters corresponding to the input images are discussed in the subsequent sections. (codeproject.com)

###### several

- We cover several advanced topics in neural networks in depth. (toronto.edu)
- The articles describes a C# library for neural network computations, and their application for several problem solving. (codeproject.com)

###### consists

- Wireless sensor network consists of a number of energy constrained sensors which are densely deployed in large geographical area. (archive.org)

###### article

- In this article, a C# library for neural network computations is described. (codeproject.com)
- Instead of this, the article assumes that the reader has general knowledge of neural networks, and that is why the aim of the article is to discuss a C# library for neural network computations and its application to different problems. (codeproject.com)
- This article will show how to use a multi-layer neural network to solve the XOR logic problem. (codeproject.com)
- Before we commence with the nitty gritty of this new article which deals with multi layer Neural Networks, let just revisit a few key concepts. (codeproject.com)

###### single

- The new network that will solve the XOR problem will look similar to a single layer network. (codeproject.com)