Abstract In recent two decades, artificial neural networks have been extensively used in many business applications. Despite the growing number of research papers, only few studies have been presented focusing on the overview of published findings in this important and popular area. Moreover, the majority of these reviews were introduced more than 15 years ago.
History[ edit ] Warren McCulloch and Walter Pitts  created a computational model for neural networks based on mathematics and algorithms called threshold logic. This model paved the way for neural network research to split into two approaches.
One approach focused on biological processes in the brain while the other focused on the application of neural networks to artificial intelligence. This work led to work on nerve networks and their link to finite automata. Hebb  created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning.
Hebbian learning is unsupervised learning. This evolved into models for long term potentiation.
Researchers started applying these ideas to computational models in with Turing's B-type machines. Farley and Clark  first used computational machines, then called "calculators", to simulate a Hebbian network. Other neural network computational machines were created by RochesterHolland, Habit and Duda With mathematical notation, Rosenblatt described circuitry not in the basic perceptron, such as the exclusive-or circuit that could not be processed by neural networks at the time.
The first was that basic perceptrons were incapable of processing the exclusive-or circuit. The second was that computers didn't have enough processing power to effectively handle the work required by large neural networks. Neural network research slowed until computers achieved far greater processing power.
Much of artificial intelligence had focused on high-level symbolic models that are processed by using algorithmscharacterized for example by expert systems with knowledge embodied in if-then rules, until in the late s research expanded to low-level sub-symbolic machine learningcharacterized by knowledge embodied in the parameters of a cognitive model.
Backpropagation distributed the error term back up through the layers, by modifying the weights at each node. Rumelhart and McClelland described the use of connectionism to simulate neural processes. However, using neural networks transformed some domains, such as the prediction of protein structures.
To overcome this problem, Schmidhuber adopted a multi-level hierarchy of networks pre-trained one level at a time by unsupervised learning and fine-tuned by backpropagation.
Once sufficiently many layers have been learned, the deep architecture may be used as a generative model by reproducing the data when sampling down the model an "ancestral pass" from the top level feature activations.
Neural networks were deployed on a large scale, particularly in image and visual recognition problems. This became known as " deep learning ".
Nanodevices  for very large scale principal components analyses and convolution may create a new class of neural computing because they are fundamentally analog rather than digital even though the first implementations may use digital devices.
Between andrecurrent neural networks and deep feedforward neural networks developed in Schmidhuber 's research group won eight international competitions in pattern recognition and machine learning.
Their neural networks were the first pattern recognizers to achieve human-competitive or even superhuman performance  on benchmarks such as traffic sign recognition IJCNNor the MNIST handwritten digits problem.
Free Essay: 1. The independent data marts have inconsistent data definitions and different dimensions and measures, 2. Which of the following is not a major. The big data trend, where companies amass vast troves of data, and parallel computing gave data scientists the training data and computing resources needed to run complex artificial neural networks. In , a neural network was able to beat human performance at an image recognition task as part of the ImageNet competition. The big data trend, where companies amass vast troves of data, and parallel computing gave data scientists the training data and computing resources needed to run complex artificial neural networks. In , a neural network was able to beat human performance at an image recognition task as part of the ImageNet competition.
Researchers demonstrated that deep neural networks interfaced to a hidden Markov model with context-dependent states that define the neural network output layer can drastically reduce errors in large-vocabulary speech recognition tasks such as voice search.
Deep, highly nonlinear neural architectures similar to the neocognitron  and the "standard architecture of vision",  inspired by simple and complex cellswere pre-trained by unsupervised methods by Hinton.
Learning is usually done without unsupervised pre-training. In the convolutional layer, there are filters that are convolved with the input. Each filter is equivalent to a weights vector that has to be trained.A neural network is a powerful computational data model that is able to capture and represent complex input/output relationships.
The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. The Best Artificial Neural Network Solution of Raise Forecast Accuracy with Powerful Neural Network Software.
The concept of neural network is being widely used for data analysis nowadays/10(1K). Learn the key concepts behind artificial neural networks.
Discover how to configure a neural network and use that network to find patterns in massive data sets. Cloud and Big Data Make Artificial Neural Networks Possible The main issue with scaling supervised learning techniques is a lack of labelled and tagged training data, but the good news is that there is a lot more data than ever before to apply to training neural networks.
Wong et al. reviewed neural network application research in business between and , stating that due to accessibility of raw data and overall complexity, financial applications could be one of the most common neural network research areas in the future.
Jul 25, · The artificial neural network behind this camera is able to learn over time, so the number of unnoticed defective products will be reduced in the future. So, artificial neural networks are capable of being used not only in the IT industry but in other fields of activity as well/5(1K).