Deep Learning In Big Data Analytics

Data Analytics, Visualizations, Data Warehousing

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19 May 2022
7 Min read

Applications & Challenges Of Integrating Deep Learning In Big Data Analytics

Deep learning applications are quickly gaining traction as the next big thing in data analytics. While AI and machine learning are rapidly evolving and will have a significant impact on the industry as a whole, deep learning is already making an impact. Data analysts are using deep learning to improve data collection and analysis. 

Data science focuses on big data analytics and deep learning. Many public and private organizations have been collecting massive amounts of domain-specific information, which can contain valuable information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Big data has become important as a result.

Companies such as Google and Microsoft, for example, are analyzing large amounts of data for business analysis and decisions, which has an impact on current and future technology.

Deep learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learned at each level of the hierarchy based on relatively simpler abstractions formulated at the previous level. Deep learning’s ability to analyze and understand massive amounts of unsupervised data is a key benefit, making it a valuable tool for Big Data Analytics where raw data is mainly unlabeled and un-categorized.

Applications Of Deep Learning In Big Data Analytics

Machine learning is a method of learning data analysis through practice. On the other hand, deep learning uses a neural network with a function similar to that of a human brain, whereas machine learning is linear. To put it another way, deep learning is a natural progression of machine learning. A series of nonlinear processing units make up deep learning. The processing units take the data from the previous units and refine the data analysis process even more. This means that data analysts can use deep learning applications to improve data collection and analysis. 

Given how much time data analysts spend prepping raw data for analysis, deep learning can be a huge help. This has interesting ramifications. Data analysts can spend less time on mundane tasks and more time on more complex ones. Productivity will rise, and operations will run more smoothly. This is because deep learning applications can complete twice as much work in half the time.

The following are some of the Deep learning applications that help big data analytics platforms improve data collection and analysis:

Optimizing Semantic Image & Video Tagging

The segmentation of complex data is made more accessible by sifting through raw data with specific applications. This includes data in various formats, such as images and videos. Semantic image and video tagging is one of many uses for deep learning in deep learning applications.

Deep learning algorithms can complete complex tasks such as video data tagging. It’s the process of locating critical scenes in large video streams. Through independent analysis, deep learning applications learn crucial data features. Because image and video data is rapidly transmitted, the ability to quickly identify key images and scenes is advantageous.

Semantic Indexing 

One of the most important aspects of data collection and analysis is information retrieval. However, as big data grows in size, storing and analyzing it becomes more difficult. The problem isn’t just one of volume. Text, image, and video are all examples of big data formats.

In addition, data analysts must analyze data in real-time. Here’s where semantic indexing comes in handy. It speeds up data analysis by allowing for quick identification of key data points. Because of their unique layout, deep learning applications are essential for semantic indexing.

Conducting Discriminative Tasks 

Learning algorithms allow users to extract complicated nonlinear features from raw data while performing discriminative tasks in Big Data Analytics. It also makes it easier to use linear models with extracted features as input to perform discriminative tasks. 

This method has two benefits: First, using Deep Learning to extract features adds non-linearity to data analysis, tying discriminative tasks to AI, and second, applying linear analytical models to extracted features is more computationally efficient. 

These two advantages are critical for Big Data because they enable practitioners to complete complex Artificial Intelligence tasks such as object recognition in images, image comprehension, and so on.

Deep Learning Challenges In Big Data Analytics

A few of the major challenges of deep learning in big data analytics are as follows:

Incremental Learning For Non-Stationary Data

Dealing with streaming and fast-moving input data is one of the most challenging aspects of big data analytics. Deep learning must be adapted to handle streaming data because algorithms that can deal with large amounts of continuous input data are required. Data analysis like this helps monitor tasks like fraud detection.

High-Dimensional Data

When dealing with high-dimensional data, such as images, some deep learning algorithms can become prohibitively computationally expensive due to the often slow learning process associated with a deeply layered hierarchy of learning data abstractions and representations from a lower to a higher-level layer. 

That is to say, when working with big data that has a lot of volumes, one of the four Vs associated with big data analytics, these deep learning algorithms can get stuck. A high-dimensional data source increases the volume of raw data while also making learning from it more difficult.

Large-Scale Simulations

How do we scale deep learning’s recent successes too much larger-scale models and massive datasets in computation and analytics? The effectiveness of large-scale models has been demonstrated empirically, focusing on models with a large number of model parameters that can extract more complicated features and representations.

Conclusion 

Deep learning assists in the automatic extraction of complex data representations from large volumes of unsupervised data. This makes it a valuable tool for big data analytics, which involves analyzing data from vast collections of unsupervised and un-categorized raw data.

Deep learning simplifies big data analytics tasks, particularly when it comes to analyzing large amounts of data, semantic indexing, data tagging, information retrieval, and discriminative tasks like classification and prediction.

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