Three Phenomenal Applications of Deep Learning in 2021

Deep learning has acquired a great significance in the last few years. Credit not only goes to the advancements in machine learning and artificial intelligence but also to artificial neural networks that have made deep learning a research oriented discipline. The motivation behind deep learning is to come up with systems and technologies that are capable of imitating the functions of the human brain. For this, deep learning makes use of artificial neural networks that perform similar functions like the neurons in the human brain. Different types of algorithms are used to perform operations on different data sets.

In order to master different techniques related to deep learning, we need to choose the best deep learning courses in the present industrial market. These courses can help us in applying different concepts of deep learning to various operational sectors. Let us take a cursory glance at such sectors and the related applications.

The increasing popularity of self driving cars

The most popular company in the world that turned the idea of self-driving cars into a reality is Tesla Motors. The journey from the design, development and research stages to the formulation of a prototype was not easy. Machine learning and deep learning systems played a great role all along the journey of research and development related to self driving cars.

The role of machine learning and deep learning started from the stage of design and development itself. With the analysis of a large number of data sets, we were able to arrive at the perfect shape of self-driving cars that was in consonance with aerodynamic arrangements. In addition to this, the trial process of self-driving cars also involved various prospects of deep learning.

The data sets that we acquired from the sensors fitted over self-driving cars were passed through artificial neural networks so that they could be processed in real time. After this, we were able to to program the self driving cars with basic features like automated braking system and other safety features. These safety features required constant updation.

Moreover, the pre-programming of self-driving cars was never going to be enough. This is where deep learning allowed the self-driving prototype cars to learn from their mistakes. The estimation of correct distance of stoppage and other ethical challenges were overcome with the help of deep learning technology. In this way, deep learning provided a concrete road map for the arrival of self-driving cars into the industrial market.

Solving the sentiment analysis challenge

Natural language processing capabilities witnessed a rapid advancement with the arrival of deep learning technology. Different types of chatbots and virtual assistants were equipped with state-of-the-art natural language capabilities. This allowed virtual assistants to understand the crux of linguistics in a great detail.

Moreover, the research in deep learning technology has enabled us to carry out sentiment analysis. Sentiment analysis is all about understanding the emotions of human beings by understanding the intention of their language. Once we are able to classify sentiments into positive, negative and neutral categories, we can arrive at a general feedback from the customers.

This can enable us to devise a grievance redressal mechanism to address the concerns of the customers that have given negative feedback. The process of sentiment analysis can also be used to understand and evaluate the performance of a particular product or a service that has been launched into the market for the purpose of trial and testing.

The precision in healthcare and diagnostic techniques

With the help of deep learning technologies, the scope and frontiers of different types of diagnostic techniques have been widely extended. For instance, deep learning technology is used to collect and analyze different types of data sets from wearable devices. This can then be used to estimate different health conditions like blood sugar level, heartbeat counts, blood flow and number of calories burnt.

It also gives us an estimate of the sleep cycle. The monitoring of the sleep cycle acts as a precursor to a sound health. In addition to this, the process of deep learning technology is also crucial for examination of medical records and other tests like CCTV scans. Deep learning technologies are also used to analyze the historic records of a patient under critical surveillance and examination. It needs to be noted at this point in time that deep learning also proved to be a notable technique in the analysis of covid-19 data.

Concluding remarks

The number of sectoral applications of deep learning would further increase in the coming times. This calls for necessary preparations in the form of online training and courses so that we stay ahead in the technological race.

This can enable us to devise a grievance redressal mechanism to address the concerns of the customers that have given negative feedback. The process of sentiment analysis can also be used to understand and evaluate the performance of a particular product or a service that has been launched into the market for the purpose of trial and testing.