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Three Applications of Deep Learning in Big Data Analytics


18 April 2017

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Three Applications of Deep Learning in Big Data Analytics

Deep Learning algorithms have great potential for research into the automated extraction of complex data representations. Deep Learning algorithms can develop a layered, and hierarchical architecture of learning and representing data. Deep Learning in Big Data Analytics has become a high-focus of data science.

Big Data has now become important as several organizations are collecting massive amounts of domain-specific information that can be used to solve problems related to national intelligence, cyber security, fraud detection, marketing, and medical informatics. Deep Learning Big Data allows extraction of high-level, complex abstractions as data representations through a hierarchical learning process. A key benefit of Deep Learning is Big Data analysis that it can learn from massive amounts of unsupervised data. This makes it a valuable tool for Big Data Analytics where huge amounts of raw data are uncategorized.


Mining and extracting meaningful patterns from large data sets for decision-making, and prediction are critical aspects of Big Data analytics. But, Big Data Analytics poses some challenges during data mining and extraction, such as format variation of the raw data, trustworthiness of data analysis, fast-moving streaming data, highly distributed input sources, and imbalanced input data. Deep Learning algorithms can address these challenges.


Information retrieval is a key task that is associated with Big Data Analytics. Efficient storage and retrieval of information is a growing problem in Big Data analysis, as data in large-scale quantities such as text, image, video, and audio is being collected and made available across various domains. Thus, strategies and solutions that were previously used for information storage and retrieval are challenged by massive volumes of data. Semantic indexing proves to be an efficient technique as it facilitates knowledge discovery and comprehension, thereby making search engines work more quickly and efficiently.


While performing discriminative tasks in Big Data Analytics, Learning algorithms allow users to extract complicated nonlinear features from the raw data. It also facilitates the use of linear models to perform discriminative tasks using extracted features as input. This approach has two advantages: Firstly, by extracting features with Deep Learning adds nonlinearity to the data analysis, thereby associating discriminative tasks closely to AI, and secondly applying linear analytical models on extracted features is more efficient computationally. These two benefits are important for Big Data because it allows practitioners in accomplishing complicated tasks related to Artificial Intelligence like object recognition in images, image comprehension, etc.


Deep Learning techniques help in semantic tagging. Deep Learning mechanisms can facilitate segmentation and annotation of complex image scenes. Deep Learning can also be used for action scene recognition as well as video data tagging. It uses an independent variant analysis to learn invariant spatiotemporal features from video data. This approach helps in extracting useful features for performing discriminative tasks on image and video data. Deep Learning has been successful in achieving remarkable results in extracting useful features. However, there is still considerable work that remains to be done for further exploration that includes determination of appropriate objectives in learning good data representations and performing other complex tasks in Big Data Analytics.

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