Deep learning applications are becoming the next big trend in data analytics. While AI and machine learning are developing at a rapid rate and will have an impact on the industry as a whole, deep learning is already making a tangible mark on the industry. Data analysts are using deep learning to optimise data collection and analysis. With 2020 drawing to a close, it is worth studying the trends that will define big data analytics in the future.
First, it is worth revisiting what deep learning is and how it’s different from machine learning. Machine learning is an algorithm that learns data analysis with experience. However, while machine learning is linear, deep learning uses a neural network where the function is similar to a human brain. In other words, deep learning is an evolution of machine learning. Deep learning is a series of non-linear processing units. The processing units take the infor mation from previous units and further refine the data analysis process.
This means data analysts improve data collection and analysis using deep learning applications. Deep learning can be a huge asset, seeing as data analysts spend much time prepping raw data for analysis. This leads to exciting implications. It means data analysts can spend less time on monotonous work and more time on higher-end tasks. Productivity will improve and operations will be far more efficient. This is because deep learning applications can perform twice the work in half the time.
Let us take a look at some of the deep learning applications optimising data collection and analysis in big data analytics platforms.
Similar to machine learning, deep learning applications can further refine their processes when exposed to raw data, making them an invaluable asset in data analysis.
Sifting through raw data with certain applications facilitates the segmentation of complex data. This includes data presented in different formats, like images and videos. This process, known as semantic image and video tagging, is one of many uses in deep learning applications.
Deep learning algorithms perform demanding tasks, like video data tagging. It is the process of finding key scenes in large streams of video data. Deep learning applications learn crucial features connected to data through independent analysis. Image and video data streams fast, so the ability to pick out key images and scenes in quick time is helpful.
Information retrieval is one of the key tasks of data collection and analysis. However, as big data expands, storing and analysing data becomes tough. It is not just volume that is an issue. Big data comes in different formats, like text, images, and video.
Furthermore, data analysts have to analyse data in real-time. This is where semantic indexing becomes useful. It allows for quick identification of key data points, accelerating the rate of data analysis. Deep learning applications are integral to semantic indexing because of their unique layout.
Deep learning applications can execute complex tasks, like object recognition in images. Analysts use a process called “Discriminative tasks” to be more precise in data analysis. Discriminative tasks come in two methods: first, nonlinearity methods in data analysis. Second, by using non-linear methods to analyse data. Linear and non-linear methods improve data analysis because data analysts can easily pull off complex tasks.
Deep learning applications can optimise data analysis across different industries. In finance, deep learning can predict future price movements and better anticipate market movements. Deep learning applications will be a valuable asset to healthcare, as well. This is because data analysts can build complex applications, like monitoring parameters and non-invasive diagnostics, around the technology.
Deep learning applications are going to play a huge role in data collection and analysis. The ability to filter data analysis through multiple layers of processing units can refine analysis processes, making it easier to glean useful insights at a much faster rate than before. Discovering useful insights, while working with complex data is much easier when deep learning is involved because AI technology can do much of the heavy lifting in data collection and analysis.