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- A survey of deep neural network architectures and their applications. This work was supported in part the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374010, and 61403319, and the Alexander von Humboldt Foundation of Germany
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A survey of deep neural network architectures and their applications introduction on the deep learning technologies and their applications. It is aimed to provide the readers with a background on diﬀerent deep learning architectures and also the latest development as well as achievements in this area. The rest of the paper is organized as follows. In Sections 2-5, four main deep. **A** **survey** **of** **deep** **neural** **network** **architectures** **and** **their** **applications** we discuss some widely-used **deep** learning **architectures** **and** **their** practical **applications**. An up-to-date overview is provided on four **deep** learning **architectures**, namely, autoencoder, convolutional **neural** **network**, **deep** belief **network**, **and** restricted Boltzmann machine. Different types of **deep** **neural** **networks** are surveyed. Request PDF | A survey of deep neural network architectures and their applications | Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep learning techniques.

A survey of deep neural network architectures and their applications and recommendation systems. In this paper, we discuss some widely-used deep learning architectures and their practical applications. An up-to-date overview is provided on four deep learning architectures, namely, autoencoder, convolutional neural network, deep belief network, and restricted Boltzmann machine. Different. In this paper, we discuss some widely-used deep learning architectures and their practical applications. An up-to-date overview is provided on four deep learning architectures, namely, autoencoder, convolutional neural network, deep belief network, and restricted Boltzmann machine. Different types of deep neural networks are surveyed and recent.

** In this paper, we discuss some widely used deep learning architectures and their practical applications**. An up-to-date overview is provided on four deep learning architectures, namely, autoencoder, convolutional neural network, deep belief network, and restricted Boltzmann machine. Different types of deep neural networks are surveyed and recent. DOI: 10.1016/j.neucom.2016.12.038 Corpus ID: 207116476. A survey of deep neural network architectures and their applications @article{Liu2017ASO, title={A survey of deep neural network architectures and their applications}, author={Weibo Liu and Zidong Wang and Xiaohui Liu and Nianyin Zeng and Yurong Liu and Fuad E. Alsaadi}, journal={Neurocomputing}, year={2017}, volume={234}, pages={11-26}

- Download PDF Abstract: Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition
- Three Classes of Deep Learning Architectures and Their Applications: A Tutorial Survey Li Deng Microsoft Research, Redmond, WA 98052, USA E-mail: deng@microsoft.com, Tel: 425-706-2719 Abstract— progress in (Markoff, 2012). In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same.
- Optimization of deep neural networks: a survey and unified taxonomy El-Ghazali Talbi To cite this version: El-Ghazali Talbi. Optimization of deep neural networks: a survey and unified taxonomy. 2020. hal-02570804v2
- applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efﬁcient processing of deep neural networks to improve key metrics—such as energy-efﬁciency
- Deep Neural Network (DNN) has find its way into DoA applications along with the well-known methods such as subspace-based or time difference of arrival methods, which opens-up the data-driven approach towards estimating the DoA. This paper first surveys different DNN architectures and their supporting methods and datasets that are used for.

- III. Neural Networks Artificial neural networks (ANNs) have been applied in a variety of applications related with antenna arrays [15]. The concept and development of artificial neural network is motivated from the concept of biological neurons of human brain [10]. Neural networks provide simple general purpose mathematical tools
- machine learning; spatial architectures; VLSI I. INTRODUCTION Deep neural networks (DNNs) are currently the founda- tion for many modern artificial intelligence (AI) applica-tions [1]. Since the breakthrough application of DNNs to speech recognition [2] and image recognition [3], the number of applications that use DNNs has exploded. These DNNs are employed in a myriad of applications from.
- Table 2 Architectures of deep neural network and their major application areas. Full size table . Advanced Architectures of Deep Neural Network. Owing to many flexibilities provided by the neural network, deep neural network can be expressed by a diverse set of models. These architectures are called deep models and consist of: AlexNet The net is named for the researchers. It was the earliest.
- A Non-Technical Survey on Deep Convolutional Neural Network Architectures. Authors: Felix Altenberger, Claus Lenz. Download PDF. Abstract: Artificial neural networks have recently shown great results in many disciplines and a variety of applications, including natural language understanding, speech processing, games and image data generation
- Three representative deep architectures — deep auto-encoder, deep stacking network, and deep neural network (pre-trained with deep belief network) — one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language.

1 — Feed-Forward Neural Networks. These are the commonest type of neural network in practical applications. The first layer is the input and the last layer is the output. If there is more than one hidden layer, we call them deep neural networks. They compute a series of transformations that change the similarities between cases. The. Three representative deep architectures - deep autoencoders, deep stacking networks with their generalization to the temporal domain (recurrent networks), and deep neural networks (pretrained with deep belief networks) - one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information. The Architecture of Deep Neural Network. testing results of each used neural networks, comparing of their results. 3.1 Data C оllесtіоn. The data set was collected from 11th grade. Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial Intelligence (AI) applications. Their ability to go beyond human precision has made these networks a milestone in the history of AI. However, while on the one hand they present cutting edge performance, on the other hand they require enormous computing power. For this reason, numerous optimization techniques. CNN综述文章 的翻译 [2019 CVPR] A Survey of the Recent Architectures of Deep Convolutional Neural Networks . 翻译. 综述深度卷积神经网络架构：从基本组件到结构创

- anto, Muhamad Erza and Tanuwidjaja.
- future internet Review An Updated Survey of Efﬁcient Hardware Architectures for Accelerating Deep Convolutional Neural Networks Maurizio Capra 1,* , Beatrice Bussolino 1,* , Alberto Marchisio 2, Muhammad Shaﬁque 2, Guido Masera 1 and Maurizio Martina 1,* 1 Department of Electrical, Electronics and Telecommunication Engineering, Politecnico di Torino, 10129 Torino, Italy; guido.masera@polito.i
- It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to.
- Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition

List of Deep Learning Architectures . What do we mean by an Advanced Architecture? Deep Learning algorithms consists of such a diverse set of models in comparison to a single traditional machine learning algorithm. This is because of the flexibility that neural network provides when building a full fledged end-to-end model Recurrent Neural Network Architectures Abhishek Narwekar, Anusri Pampari CS 598: Deep Learning and Recognition, Fall 2016 . Lecture Outline 1. Introduction 2. Learning Long Term Dependencies 3. Regularization 4. Visualization for RNNs. Section 1: Introduction. Applications of RNNs Image Captioning [reference].. and Trump [reference] Write like Shakespeare [reference] and more! Applications. know how to train neural networks to surpass more traditional approaches, except for a few specialized problems. What changed in 2006 was the discovery of techniques for learning in so-called deep neural networks. These techniques are now known as deep learning. They've been developed further, and today deep neural networks and deep learnin

** Deep learning techniques have found an increasing number of applications in the ﬁeld of geosciences**. Among the most applied ones, Convolutional Neural Networks stand out by their ability to extract features from grid-like topological inputs, enriching the information fed to prediction models, improving their accuracies. This tutorial seeks to explain step by step the building blocks of. In the past decade, deep neural networks have inspired waves of novel applications for machine learning problems. Recently, the biomedical field has also witnessed a surge of deep learning assisted studies, which involve protein structure prediction, gene expression regulation, protein classification, etc. [].For instance, in just 3 years, a series of deep learning models [2,3,4,5] was devised. RNN is one of the fundamental network architectures from which other deep learning architectures are built. RNNs consist of a rich set of deep learning architectures. They can use their internal state (memory) to process variable-length sequences of inputs. Let's say that RNNs have a memory. Every processed information is captured, stored, and utilized to calculate the final outcome. This. Major Architectures of Deep Networks. The mother art is architecture. Without an architecture of our own we have no soul of our own civilization. Frank Lloyd Wright. Now that we've seen some of the components of deep networks, let's take a look at the four major architectures of deep networks and how we use the smaller networks to build.

* Machine learning and deep neural networks promise to transform the practice of medicine, and in particular the practice of diagnostic radiology*. These technologies are evolving at a rapid pace due to innovations in computational hardware and novel neural network architectures. Several cutting-edge post-processing analysis applications are. Advances in computational power and availability of a large amount of data have paved the way to employ advanced neural network (NN) models for ITS, including deep architectures. There have been various NN-based approaches proposed for short-term traffic state prediction that are surveyed in this article, where the existing NN models are classified and their application to this area is. Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perception about the efficacy of deep neural architectures in complex pattern recognition tasks grows, sequentially up-to-date primers on the current state of.

The required number to call a Feedforward Neural Network (FFNN) architecture deep is debatable, but architectures with more than two hidden layers are commonly considered as deep (Yoshua, 2009). A Feedforward Neural Network, also called a Multilayer Perceptron (MLP), can use linear or non-linear activation functions (Goodfellow et al., 2016). Importantly, there are no cycles in the NN that. Despite their appealing properties and potential for opening up entirely new neural architectures, deep complex-valued neural networks have been marginalized due to limited availability of building blocks required for such model designing. This survey compactly summarizes the research efforts that demonstrates relevant applications of deep complex-valued neural networks in the realm of remote. Out of the large number of DL architectures, researchers have been mainly relying on Convolutional Neural Network (CNN)-based approaches for detecting these NLD from MRI data in comparison to other architectures such as Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM), Deep Neural Network (DNN), and Autoencoder (AE) (see Fig. 1a) Different architectures and their effectiveness to solve domain specific problems are evaluated. Various limitations and open problems of current architectures are discussed to provide better insights to help researchers and student to resume their research on these issues. One hundred one articles were reviewed for this meta‐analysis of deep learning. From this analysis, it is concluded.

NEURAL NETWORK DESIGN (2nd Edition) provides a clear and detailed survey of fundamental neural network architectures and learning rules. In it, the authors emphasize a fundamental understanding of the principal neural networks and the methods for training them. The authors also discuss applications of networks to practical engineering problems in pattern recognition, clustering, signal. Survey S. Sahoo et al-Heartbeat classification by using a convolutional neural network trained with Walsh functions Zümray Dokur and Tamer Ölmez-This content was downloaded from IP address 207.46.13.119 on 09/05/2020 at 01:29. 1 Content from this work may be used under the terms of the CreativeCommonsAttribution 3.0 licence. Any further distribution of this work must maintain attribution to. (Convolutional Neural Networks), RNN (Recurrent Neural Networks), Recursive Neural Networks, DBN (Deep Belief Networks) and many more. Neural networks are very beneﬁcial in text generation, vector representation, word representation estimation, sentence classiﬁcation, sentence modeling and fea-ture presentation. [9]. 1) Applications of Deep. Deep neural network (DNN) applications require heavy computations, so an embedded device with limited hardware such as an IoT device cannot run the apps by itself. One solution is to offload DNN computations from the client device to nearby edge servers [1] to request an execution of the DNN computations with their powerful hardware. However. Peng et al. propose a network intrusion detection method based on deep learning, which uses deep neural network to extract features of network monitoring data, and BP neural network is used to classify intrusion types. The method is evaluated by KDDCup 99 dataset. The results show that the method achieves the accuracy of 95.45%, and it has a significant improvement while compared with the.

Deep Neural Networks (DNNs) are a fundamental tool in the modern development of Machine Learning. Beyond the merits of the training algorithms, a great part of DNNs success is due to the inherent properties of their layered architectures, i.e., to the introduced architectural biases. In this tutorial, we explore recent classes of DNN models wherein the majority of connections are randomized or. * Although general surveys of this fast-moving paradigm (i*.e., deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with AlexNet as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope.

Business Applications of Neural Networks: Real-world business applications for neural networks are booming. In some cases, NNs have already become the method of choice for businesses that use hedge fund analytics, marketing segmentation, and fraud detection. Here are some neural network innovators who are changing the business landscape The aim of this survey is two fold, firstly we present a structured and comprehensive reviewof research methods in deep anomaly detection (DAD). Furthermore, we also discuss the adoption of DAD methods across various application domains and assess their effectiveness We summarize typical applications of resource-limited deep learning and point out that deep learning is an indispensable impetus of pervasive computing. Subsequently, we explore the underlying reasons for the high computational overhead of DL through reviewing the fundamental concepts including capacity, generalization, and backpropagation of a neural network. Guided by these concepts, we.

Our neural network with 3 hidden layers and 3 nodes in each layer give a pretty good approximation of our function. Choosing architectures for neural networks is not an easy task. We want to select a network architecture that is large enough to approximate the function of interest, but not too large that it takes an excessive amount of time to. This work primarily focuses on providing a comprehensive study for deepfake detection using deep-learning methods such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Long short-term memory (LSTM). This survey will be useful and beneficial for researchers in this field as it will give: 1) details summary of the current research studies; 2) datasets used in this field. **Neural** **Networks** (NN) [7, 8, 9] is a sub-field of ML and it was this sub-field that spawned **Deep** Learning (DL). Among the most prominent factors that contributed to the huge boost of **deep** learning are the appearance of large, high-quality, publicly available labelled datasets, along with the empowerment of parallel GPU computing, which enabled the transition from CPU-based to GPU-based training. * Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video*. However, RNNs consisting of sigma cells or tanh cells are unable to learn the relevant information of input data when the input gap is large. By introducing gate functions into the cell structure, the long short-term memory (LSTM) could handle the problem.

Deep Belief Networks: How They Work and What Are Their Applications - MissingLink.ai Deep belief networks are a class of deep neural networks━algorithms that are modeled after the human brain. Building Neural Networks. David M. Skapura. Addison-Wesley Professional, 1996 - Computers - 286 pages. 2 Reviews. This practical introduction describes the kinds of real-world problems neural network technology can solve. Surveying a range of neural network applications, the book demonstrates the construction and operation of artificial neural.

Recent advancements in deep learning have led to a resurgence of medical imaging and Electronic Medical Record (EMR) models for a variety of applications, including clinical decision support. If you're looking to learn about Neural Networks and their Implementation, This is one of the best books you can get your hands on, This book doesn't waste time on explaining every single point in childish ways, instead, it's very conscience and to the point like every textbook should be, everything is clearly explained and graphically plotted where required, it's just Awesome One of the ﬁrst applications of convolutional neural net-works (CNN) is perhaps the LeNet-5 network described by [31] for optical character recognition. Compared to mod-ern deep CNN, their network was relatively modest due to the limited computational resources of the time and the al-gorithmic challenges of training bigger networks. Though much potential laid in deeper CNN architectures.

The neural network architectures used throughout the survey are introduced, along with several important milestones and the use of deep learning in solving big data analytics challenges. The Deep learning methods for class imbalanced data section surveys 15 published studies that analyze deep learning methods for addressing class imbalance Neurocomputing welcomes theoretical contributions aimed at winning further understanding of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence. * TL;DR Backbone is not a universal technical term in deep learning*. (Disclaimer: yes, there may be a specific kind of method, layer, tool etc. that is called backbone, but there is no backbone of a neural network in general.) If authors use the word backbone as they are describing a neural network architecture, they mea

The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer. Learning to Prune Deep Neural Networks via Reinforcement Learning. This paper proposes PuRL - a deep reinforcement learning (RL) based algorithm for pruning neural networks. Unlike current RL based model compression approaches where feedback is given only at the end of each episode to the agent, PuRL provides rewards at every pruning step A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order.Recursive neural networks, sometimes abbreviated as RvNNs, have been successful, for instance, in.

In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural network, most commonly applied to analyze visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation equivariant. The deep learning neural networks of our team have revolutionised pattern recognition and machine learning, and are now heavily used in academia and industry. In 2020-21, we celebrate that many of the basic ideas behind this revolution were published within fewer than 12 months in our Annus Mirabilis 1990-1991 at TU Munich We will then discuss and review directions along which (deep) neural networks can be, or already have been, applied in the context of psychiatry, and will try to delineate their future potential.

New learning algorithms and architectures that are currently being developed for deep neural networks will only acceler - ate this progress. Supervised learning The most common form of machine learning, deep or not, is super - vised learning. Imagine that we want to build a system that can classify images as containing, say, a house, a car, a person or a pet. We first collect a large data set. Depending on the arrangement of neurons and their interconnection via the processing layers, the main architectures of artificial neural networks can be divided as follows: single-layer feedforward network (as example, perceptron and the ADALINE), multilayer feedforward networks (multilayer perceptron (MLP) and the radial basis function (RBF)), recurrent networks, and mesh networks (The Self. The ending of Moore's Law leaves domain-specific architectures as the future of computing. A trailblazing example is the Google's tensor processing unit (TPU), first deployed in 2015, and that provides services today for more than one billion people. It runs deep neural networks (DNNs) 15 to 30 times faster with 30 to 80 times better energy. the neural network models and apply them to their own work. This tutorial is expected to be self-contained, while presenting the di erent approaches under a uni ed notation and framework. It repeats a lot of material which is available elsewhere. It also points to external sources for more advanced topics when appropriate. This primer is not intended as a comprehensive resource for those that.

applications of neural networks can skip Chapters 5 and 6 and go directly to the backpropagation algorithm (Chapter 7). I am especially proud of this chapter because it introduces backpropagation with minimal eﬀort, using a graphical approach, yet the result is more general than the usual derivations of the algorithm in other books. I was rather surprised to see that Neural Computation. One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural networks (DNNs) for the scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting Artificial neural networks have been used in artificial intelligence since the 1950s. Advances in training techniques and network architectures, combined with the recent availability of large amounts of labeled data and powerful parallel computing hardware, have enabled rapid development of deep learning algorithms. Deep learning is a powerful.

Graph Neural Networks (GNNs), which generalize the deep neural network models to graph structured data, pave a new way to effectively learn representations for graph-structured data either from the node level or the graph level. Thanks to their strong representation learning capability, GNNs have gained practical significance in various applications ranging from recommendation, natural. An artificial neural network is a system of hardware or software that is patterned after the working of neurons in the human brain and nervous system. Artificial neural networks are a variety of deep learning technology which comes under the broad domain of Artificial Intelligence. Deep learning is a branch of Machine Learning which uses different types of neural networks Deep Neural Networks. A deep neural network (DNN) is an ANN with multiple hidden layers between the input and output layers. Similar to shallow ANNs, DNNs can model complex non-linear relationships. The main purpose of a neural network is to receive a set of inputs, perform progressively complex calculations on them, and give output to solve. used neural network architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM Cortex-M3 microcontroller, which is a platform often used in pervasive applications us-ing neural networks such as wearables, surveillance cameras, etc. Our experiments show that a side-channel attacker is capable of obtaining the following. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other.

Deep Neural Networks: The Great Riddle. We start with a primer to the core concepts behind deep learning and narrow the discussion to the context of supervised classification tasks, as highlighted in ref. 10.We are given a large training dataset of N signal examples, {x i} i = 1 N ∈ ℝ n, each belonging to one of C categories, and our goal is to use these for designing a machine that. Deep learning has been shown successful in a number of domains, ranging from acoustics, images to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, a significant amount of research efforts have been devoted to this area, greatly advancing graph analyzing techniques. In this survey.

About. Deep learning is primarily a study of multi-layered neural networks, spanning over a great range of model architectures. This course is taught in the MSc program in Artificial Intelligence of the University of Amsterdam. In this course we study the theory of deep learning, namely of modern, multi-layered neural networks trained on big data In this post you will discover amazing and recent applications of deep learning that will inspire you to get started in deep learning. allowing the algorithm to learn the dependencies between words and their mapping to a new language. Stacked networks of large LSTM recurrent neural networks are used to perform this translation. As you would expect, convolutional neural networks are used to. Deep Learning Examples. In recent years, multiple neural network architectures have emerged, designed to solve specific problems such as object detection, language translation, and recommendation engines. These architectures are further adapted to handle different data sizes, formats, and resolutions when applied to multiple domains in medical. Abstract. Deep neural networks have been successfully applied in many different fields like computational imaging, healthcare, signal processing, or autonomous driving. In a proof-of-principle study, we demonstrate that computational optical form measurement can also benefit from deep learning. A data-driven machine-learning approach is explored to solve an inverse problem in the accurate.

Other RNN Architectures . Need for a Neural Network dealing with Sequences. Before we deep dive into the details of what a recurrent neural network is, let's ponder a bit on if we really need a network specially for dealing with sequences in information. Also what are kind of tasks that we can achieve using such networks. The beauty of recurrent neural networks lies in their diversity of. Principles of graph neural network Updates in a graph neural network • Edge update : relationship or interactions, sometimes called as 'message passing' ex) the forces of spring • Node update : aggregates the edge updates and used in the node update ex) the forces acting on the ball • Global update : an update for the global attribute ex) the net forces and total energy of the.

With the development of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have emerged to wireless communication system, especially in cybersecurity. In this paper, we offer a review on attack detection methods involving strength of deep learning techniques. Specifically, we firstly summarize fundamental problems of network security and attack. Deep learning, a machine learning approach using deep neural networks, is becoming popular for solving artificial intelligence tasks. Deep neural networks (DNNs) have been successful in various tasks such as image recognition (LeCun et al., 1998; Krizhevsky et al., 2012), speech recognition (Hinton et al., 2012), and reinforcement learning tasks (Mnih et al., 2013, 2015) Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling Has¸im Sak, Andrew Senior, Franc¸oise Beaufays Google, USA fhasim,andrewsenior,fsb@google.comg Abstract Long Short-Term Memory (LSTM) is a speciﬁc recurrent neu-ral network (RNN) architecture that was designed to model tem-poral sequences and their long-range dependencies more accu-rately than. **Neural** Computing & **Applications** is an international journal which publishes original research and other information in the field of practical **applications** **of** **neural** computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to Hence in future also neural networks will prove to be a major job provider. How this technology will help you in career growth. There is huge career growth in the field of neural networks. The average salary of a neural network engineer ranges from $33,856 to $153,240 per year approximately. Conclusion. There is a lot to gain from neural. The ancient term Deep Learning was first introduced to Machine Learning by Dechter (1986), and to Artificial Neural Networks (NNs) by Aizenberg et al (2000). Subsequently it became especially popular in the context of deep NNs, the most successful Deep Learners, which are much older though, dating back half a century. This article will focus on essential developments since the 1960s.