Big Data Analytics and Deep Learning are two areas of data science that are receiving a lot of attention these days. Many public/private organizations have been collecting massive amounts of domain-specific information that can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing and medical informatics. Google and Microsoft, for example, are evaluating vast amounts of data for business analysis and decisions, which has an impact on current and future technologies. Through a hierarchical learning process, Deep Learning algorithms extract high-level, complex abstractions as data representations. Complex abstractions are learned based on simpler abstractions. The analysis and learning of enormous amounts of unsupervised data is a core benefit of Deep Learning, making it a powerful tool for Big Data Analytics when raw data is largely unlabeled and un-categorized. We look forward to how Deep Learning may be used to solve certain key Big Data Analytics concerns, such as extracting complicated patterns from large amounts of data, semantic indexing, quick information retrieval, and simplifying discriminative tasks. Several parts of Deep Learning need more investigation to incorporate specific Big Data Analytics concerns, such as streaming data, high-dimensional data and model scalability and distributed computing.
Deep Learning algorithms and architectures are more adapted to address difficulties linked to Volume and Variety of Big Data Analytics when considering each of the four Vs of Big Data characteristics, namely Volume, Variety, Velocity, and Veracity. Where algorithms with shallow learning hierarchies struggle to explore and understand the greater complexities of data patterns, Deep Learning intrinsically uses the availability of huge volumes of data, i.e. Volume in Big Data. Furthermore, because Deep Learning deals with data abstraction and representations, it is likely suited for analyzing raw data presented in various formats and/or from various sources, i.e. Big Data variety, and may reduce the need for human experts to extract features from each new data type observed in Big Data. While more traditional data analysis methodologies have problems, Big Data Analytics gives a significant potential for building unique algorithms and models to handle specific Big Data issues. For data analytics specialists and practitioners, Deep Learning ideas give one such solution venue. For example, Deep Learning’s extracted representations can be used as a practical source of knowledge in Big Data Analytics for decision-making, semantic indexing, information retrieval and other purposes and simple linear modeling techniques can be used in Big Data Analytics when complex data is represented in higher levels of abstraction.
Deep Learning, in contrast to more traditional machine learning and feature engineering methods, has the advantage of potentially giving a solution to data analysis and learning challenges found in large amounts of data. It assists in the automatic extraction of complicated data representations from vast volumes of unsupervised data. This makes it a useful tool for Big Data Analytics, which entails analyzing data from very huge amounts of unstructured and un-categorized raw data. Deep Learning’s hierarchical learning and extraction of different levels of complex data abstractions simplify Big Data Analytics tasks, particularly for analyzing large amounts of data, semantic indexing, data tagging, information retrieval, and discriminative tasks like classification and prediction.
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