Review on Deep Learning for Big Data: Challenges and Perspectives (original) (raw)
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Big Data Deep Learning: Challenges and Perspectives
Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. In this paper, we provide a brief overview of deep learning, and highlight current research efforts and the challenges to big data, as well as the future trends.
Восточно Европейский Научный Журнал, 2020
Over the last few years, Deep learning has begun to play an important role in analytics solutions of Big Data. Deep learning is one of the most active research fields in machine learning community. It has gained unprecedented achievements in fields such as computer vision, natural language processing and speech recognition. The ability of deep learning to extract high-level complex abstractions and data examples, especially unsupervised data from large volume data, makes it attractive a valuable tool for Big Data analytics. In this paper, discuss the challenges posed by Big Data analysis. Next, presented typical deep learning models, which are the most widely used for Big Data analysis and feature learning. Finally, have been outlined some open issues and research trends.
A survey on deep learning in big data analytics
Industry 4.0, 2020
Over the last few years, Deep learning has begun to play an important role in analytics solutions of big data. Deep learning is one of the most active research fields in machine learning community. It has gained unprecedented achievements in fields such as computer vision, natural language processing and speech recognition. The ability of deep learning to extract high-level complex abstractions and data examples, especially unsupervised data from large volume data, makes it attractive a valuable tool for big data analytics. In this paper, we review the deep learning architectures which can be used for big data processing. Next, we focus on the analysis and discussions about the challenges and possible solutions of deep learning for big data analytics. Finally, have been outlined several open issues and research trends.
A REVIEW OF IMPLEMENTATION OF DEEP LEARNING IN BIG DATA ANALYSIS
Data is being generated very rapidly and at very high magnitudes. This data, termed as 'Big Data' has found its use in many fields. The aim of this paper is to discuss the challenges posed by Big Data analysis and the techniques that can be used to solve these challenges. One of the most efficient techniques used to do so is Deep Learning. This paper also focuses on the models that have been implemented for big data analysis with deep learning and its applications.
Deep learning and Big Data Analysis: Challenges, Opportunities and Applications
With the approach of IoT, there are tremendous changes going in the measure of information. and deep learning are the two most critical concentration focuses in this universe of computerized science. Information is considered an indispensible need these days. This structure of open and private information is likewise exceptionally import primary favourable position of DL is looking at and gets out taking in measures from it for unsupervised and managed data, by making it a noteworthy gadget for Big Data investigation where crude information is accumulated and is marked and unlabel long last arranged. In the present paper, we examine with reference to how Deep Learning is exploited for some basic issues in Big Data Analytics, which additionally incorporates extraction of complex information from colossal masses of data requesting, data marking, speedy information recuperation, and enhancing discriminative errands. Huge information has now ended up being extremely famous as a couple of affiliations are gathering huge measures of room specific information which utilized to address the issues related to national learning, computerized security, distortion area, and wellbeing informatics. Profound Learning Big Data permits extraction of not typical condition of information, complex thoughts as data depiction through a different levelled learning process. A key favourable position of Deep Learning is Big Data investigation examination that it can pick up from huge mass of unsupervised data. In this manner, making it an imperative instrument for Big Data Analytics where monstrous measures of data and information are not group. This part weights on the need for immense information, mechanical kinds of @ IJTSRD |
Deep learning applications and challenges in big data analytics
Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.
OPPORTUNITIES AND GAINSAYS IN DEEP LEARNING ON BIG DATA
Deep learning is currently an extremely active research area in pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, Deep learning is coming to play a key role in providing big data predictive analytics solutions.Big data assist ML algorithms to uncover more fine-grained patterns and helps in accurate predictions .The major challenges to ML are model scalability and distributed computing.The realization of this grand potential relies on the ability to extract value from such massive data through data analytics; Deep learning is at its core because of its ability to learn from data and provide data driven insights, decisions, and predictions. In this paper first, we review the Deep learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning and analyse the challenges and possible solutions of Deep learning for big data. Finally, we outline several open issues and research trends.
Deep Learning Techniques, Applications and Challenges: An Assessment
International Journal of Trend in Scientific Research and Development, 2018
Deep learning is an emerging research area in machine learning and pattern recognition field. Deep learning refers to machine learning techniques that use supervised or unsupervised strategies to automatically learn hierarchical representations in deep architectures for classification. The objective is to discover more abstract features in the higher levels of the representation, by using neural networks which easily separates the various explanatory factors in the data. In the recent years it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. This paper presents a brief overview of deep learning, techniques, current research efforts and the challenges involved in it.
DEEP LEARNING BASED ON BIG DATA ANALYTICS
IAEME PUBLICATION, 2020
Deep learning techniques are widely extended to numerous scientific disciplines and information technology like voice recognition, object definitions, and learning processes in visual processing. Likewise, conventional data analysis methods have many constraints of processing massive amount of information. Deep Learning is actually a rather emerging field in neural networks and culture of artificial intelligence. It has gained enormous success in important field technologies like Machine learning, Voice and Video Analysis, and Machine Translation Processing. There are huge quantities of information produced by numerous sources daily. Therefore, the data concept is translated to Analytics that poses difficulties in the phases of knowledge processing and judgment-making. Furthermore, Big Data analytics needs new and advanced methodologies depending on system and deep learning methods to analyze data in real-time with high reliability and productivity. Deep learning skills can promote the handling of such information, particularly their capacity to handle both the marked and unmarked data which are sometimes amply gathered in Big Data. The paper provides a comprehensive of Big Data and discusses particular problems in analytics that Deep Learning can solve.
IJERT-A Depth of Deep Learning for Big Data and its Applications
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/a-depth-of-deep-learning-for-big-data-and-its-applications https://www.ijert.org/research/a-depth-of-deep-learning-for-big-data-and-its-applications-IJERTCONV8IS10004.pdf Although Machine Learning (ML) has become synonymous for Artificial Intelligence (AI); recently, Deep Learning (DL) is being used in place of machine learning persistently. While machine learning is busy in supervised and unsupervised methods, deep learning continues its motivation for replicating the human nervous system by incorporating advanced types of Neural Networks (NN).. If we apply Deep Learning to Big Data, we can find unknown and useful patterns that were impossible so far. Deep Learning is applied in self driving cars, visual recognition, healthcare, transportation etc. Nowadays, companies have started to realize the importance of data availability in large amounts in order to make the correct decision and support their strategies. Big Data means extremely huge large data sets, which is heterogeneous whose characteristics (large volume, different forms, speed of processing), analyzed to find the patterns, trends. This paper provides an introductory tutorial to the domain of deep learning for Big Data with its history, evolution, and introduction to some of the sophisticated neural networks such as Deep belief network , Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).