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Deep Learning Applications And Challenges In Big Data Analytics Pdf

deep learning applications and challenges in big data analytics pdf

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Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

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.

Interest in big data has swelled within the scholarly community as has increased attention to the internet of things IoT. Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students. Buy Hardcover. Add to Cart.

We discuss the new challenges and directions facing the use of big data and artificial intelligence AI in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms.

Deep learning applications and challenges in big data analytics

This section of the Web site provides theses and projects proposals for students. The goal of this thesis is to identify novel Bayesian Optimization methods to build performance models for various big data and deep learning applications based on Spark, the most promising big data framework which will probably dominate the big data market in the next years. The aim of this research work is to building accurate machine learning models to estimate the performance of Spark applications possibly running on GPU clusters by considering only few test runs on reference systems and identify optimal or close to optimal configurations. Bayesian methods will be mixed with traditional techniques for performance modelling, which includes computer systems simulations or bounding techniques. Nowadays, Big Data are becoming more and more important. Many sectors of our economy are now guided by data-driven decision processes.

deep learning applications and challenges in big data analytics pdf

Machine Learning With Big Data: Challenges and Approaches

Big data is no mere buzzword—big data is here to stay. Organizations that bypassed the initial hype now see the need to make decisions as to whether to intertwine big data with their future organizational culture. Others are experiencing pressure from the first movers.


The use of deep learning DL for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper or more hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health mHealth. DL can provide the analysis for the deluge of data generated from mHealth apps.

Metrics details. 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.

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    12.04.2021 at 21:27

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