Survey: A wide and deep neural network with their implementation
0
Citation
0
Reference
20
Related Paper
Abstract:
The notion concerning a neural community capable concerning transcribing ethnical composition has long gone beyond a list in accordance with turning into an almost insignificant task. Neural networks started out abroad as like simply a mathematical concept, no longer something that may want to remain performed together with the technological know-how degree of the time, however above period the thoughts grew and the science eventually caught up. ANNs commenced including an assignment via McCullogh and Pitts whichever described up to expectation sets concerning easy devices (artificial neurons) could operate entire viable logic operations or hence stay capable regarding normal computation. In 1985, Rumelhart, McClelland, yet Hinton determined a powerful study regime that allowed them to educate ANNs together with various black units. Actually, flagrant neural networks (DNNs) are awfully utilized because of extraordinary features and have performed state-of-the-art performances.in it record review an overview present day concerning the research touching DNNs’ implementations are presented. As because the large awful neural networks, we showed the purpose for the appearance of this kind regarding network, the features, the architecture over these networks, the learning strategies used, as differentiates this networks beside the traditional networks. After the suggestion of an algorithm of fast learning for deep networks through 2006, the deep learning methods have induced regularly-growing study attention for the reason of their intrinsic ability to overcome the disadvantage of classical algorithms conditional on manually-prepared characteristics. Deep learning strategies have further been discovered to be proper for huge data examination with prosperous applications to computer vision, pattern recognition, etc. In this paper, we consider several architectures of widely-used deep learning with their functional applications. A modern summary is presented on some deep learning architecture, wide Deep Neural Networks Implementations, traditional neural network, embedding vector. Various kinds of deep neural networks are viewed and modern forwards are compiled. Employment of deep learning methods on remarkable chosen fields also analyzed.Keywords:
Deep Neural Networks
Implementation
Disadvantage
Cite
Deep neural network–based models are gradually becoming the backbone for artificial intelligence and machine learning implementations. The future of data mining will be governed by the usage of artificial neural network–based advanced modeling techniques. One obvious question is why neural networks are only now gaining so much importance, because it was invented in 1950s.
Implementation
Deep Neural Networks
Cite
Citations (8)
The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.
Deep belief network
Instance-based learning
Cite
Citations (1,139)
The brain's operation depends on networks of nerve cells, called neurons, connected with each other by synapses. Scientists can now mimic some of the brain's behaviours with computer-based models of neural networks. One major domain of behaviour to which this approach has been applied is the question of how the brain acquires and maintains new information; that is, what we would call learning and memory. Neural networks employ various learning algorithms, which are recipes for how to change the network to store new information, and this chapter surveys learning algorithms that have been explored over the last decade. A few representative examples are presented here to illustrate the basic types of learning algorithms; the interested reader is encouraged to consult recent books listed in the section on Further reading, which present these algorithms in greater detail and provide a more complete survey.
Nerve cells
Cite
Citations (8)
Deep learning has solved a problem that as little as five years ago was thought by many to be intractable - the automatic recognition of patterns in data; and it can do so with accuracy that often surpasses human beings. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners trying to make sense out of the flood of data that now inundates our society. As public awareness of the efficacy of DL increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge produced by experts in the field. Where does one start? How does one determine if a particular model is applicable to their problem? How does one train and deploy such a network? A primer on the subject can be a good place to start. With that in mind, we present an overview of some of the key multilayer ANNs that comprise DL. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is becoming critical to many computer applications, we include a section on using neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where DL has emerged as a game-changing technology: anomalous behavior detection in financial applications or in financial time-series forecasting, predictive and prescriptive analytics, medical image processing and analysis and power systems research. The thrust of this review is to outline emerging areas of application-oriented research within the DL community as well as to provide a reference to researchers seeking to use it in their work for what it does best: statistical pattern recognition with unparalleled learning capacity with the ability to scale with information.
Realm
Predictive Analytics
Subject-matter expert
Cite
Citations (7)
Cite
Citations (13)
The rise of big data and big compute has brought modern neural networks to many walks of digital life, thanks to the relative ease of constructing large models that scale to the real world. Current successes of Transformers and self-supervised pretraining on massive data have led some to believe that deep neural networks will be able to do almost everything once we have sufficient data and computational resources. However, neural networks are fast to exploit surface statistics but fail miserably to generalize to novel combinations. This is because they are not designed for deliberate reasoning -- the capacity to deliberately deduce new knowledge out of the contextualized data. This tutorial reviews recent developments to extend the capacity of neural networks to "learning-to-reason'' from data, where the task is to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary compositional querying without the need of predefining a narrow set of tasks. The tutorial consists of four parts. The first part covers the learning-to-reason framework, and explains how neural networks can serve as a strong backbone for reasoning through its natural operations such as binding, attention & dynamic computational graphs. The second part goes into more detail on how neural networks perform reasoning over unstructured and structured data, and across modalities. The third part reviews neural memories and their role in reasoning. The last part discusses generalization to novel combinations, under less supervision and with more knowledge.
Cite
Citations (2)
Autoencoder
Overfitting
Restricted Boltzmann machine
Deep belief network
Cite
Citations (99)
This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in face recognition is described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the applications of neural Networks. I. Introduction Throughout the years, the computational changes have brought growth to new technologies. Such is the case of artificial neural networks, that over the years, they have given various solutions to the industry. Designing and implementing intelligent systems has become a crucial factor for the innovation and development of better products for society. Such is the case of the implementation of artificial life as well as giving solution to interrogatives that linear systems are not able resolve. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. A trained neural network can be thought of as an “expert” in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer “what if” questions. II. Use of neural networks
Cite
Citations (0)
An artificial neural network is a basic building block for deep learning. Understanding the neural network and making intuitive sense of them is a major challenge for anyone who wants to use them. This is partly due to incomplete articles and highly technical and mathematical papers. Although understanding papers are the best way to completely grasp the ideas, it's very intimidating to beginners. Most of the articles available online are not complete in the sense that they fail to provide intuition about nuances of neural networks at a deeper level. It is important to understand the neural network at a deeper level to make practical use of it. With partial knowledge application of neural networks falls apart at multiple levels from choosing cost function to the learning rate. In this paper, we have covered major concepts of neural networks which is important to fully understand neural networks functionality. This paper also contains a zoomed-in view of each part of mathematics through equations and python code.
Cite
Citations (0)
Neural Network technology performs intelligent tasks similar to those performed by the human brain. Today, many researchers are investigating Neural Networks, the network holds great potential as the front - end of system that require massive amount of inputs from sensor as well as real - time response. Neural Networks has been successfully applied to broad spectrum of data - intensive applications, such as; Process modeling and control, Machine diagnosis, Medical diagnosis, Voice Recognition, Financial forecasting, Fraud detection. In this paper presentation, real - world applications of neural network was considered including Traveling Salesman Problem Routes. Elements of an Artificial Neural System (ANS), Characteristics of (ANS), Historical Developments in (ANS) Technology, Applications of (ANS) Technology, Commercial Development in (ANS), Neural Networks versus conventional computers, etc was also given due consideration. There was a new development in programming paradigm, which arose in the 1980's. This new development was based on how the human brain processes information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. It was sometimes called connectionism since it models solution to problem by training simulated neurons connected in a network. Neural Network has proven to be a powerful data modeling tool 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. Neural Network achieved this by; acquiring knowledge via learning and sharing the learnt Knowledge within inter - neuron connection strengths generally know as Synaptic Weights. An Artificial Neural Network (ANN), usually called Neural Network has been found to hold great potential as the front - end of system and have made remarkable success in providing real time response to complex pattern recognition problems. Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an expert in the category of information it has been given to analyze. This can then be used to provide projections given new situations of interest and answer what if questions. Other reasons why we make use of Neural Networks include; Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
Connectionism
Cite
Citations (3)