As you may have noticed, high-tech concepts such as Deep Learning have emerged over the past few years, as well as their adoption by some tech giants.

Naturally, one would wonder why Deep Learning has become the center of attention among business owners worldwide. In this post, we will examine Deep Learning in detail and determine the reasons for its increasing popularity.

Deep Learning: What is it?

Put simply, Deep Learning is a subset of machine learning that enables machines to mimic the inherent human learning process through examples. This technology can be described as a set of algorithms that can learn in layers and allow a computer system to develop a hierarchy of complex concepts from simple concepts that can be easily understood.

Models learn to perform tasks accurately by analyzing text, sound, and image. Unlike products powered by traditional machine learning technology, this technology powers some of the high-end innovations available such as driverless cars, voice control in various devices, and so much more.

Uses of Deep Learning in real-world applications

Many real-world Deep Learning applications are part of our daily lives, and industries use Deep Learning to leverage its benefits each day. Let’s have a look at some of them:

• Law enforcement: Law enforcement uses Deep Learning algorithms to learn from transactional data and identify patterns that would suggest possible future criminal activities.

In addition, they utilize several Deep Learning applications to analyze data quickly and effectively investigate crimes by extracting patterns from audio and visual recordings, images, and documents.

• Financial institutions: Predictive analytics is used by several reputable financial institutions to drive the algorithmic trading of stocks, assess business risks before approving loans, detect fraudulent activities, manage investment portfolios, and many other activities.
• Customer service: Deep Learning technology is incorporated into several customer service processes to engage users in a personalized manner. This can be seen in the integration of chatbots into applications and customer service portals. Through training and learning, these chatbots determine if they can provide multiple responses to questions and decide whether they can answer the question on their own or re-route the question to a human customer service executive.
• Healthcare: After digitizing hospital records and images, healthcare providers have benefited significantly from Deep Learning. Now, through image recognition applications, healthcare providers can support medical imaging specialists and radiologists with analyzing images accurately in a shorter period.

Importance of Deep Learning

Deep Learning can assist us in achieving breakthroughs and has proven to be valuable at a time when opportunities are rare and unprecedented. Researchers have used Deep Learning to discover exoplanets and novel drugs, as has the detection of subatomic particles and diseases.

Our biological understanding in all fields is being enriched, including genomics, proteomics, metabolomics, immunome, and other areas. Additionally, cameras serving as our artificial eyes and neural networks capable of processing the visual information they capture are leading to a proliferation of data-driven artificial intelligence applications.

Likewise, Deep Learning and neural networks will enhance robot abilities just as vision plays a crucial role in the evolution of life on earth. Through increasing confidence levels, they will be able to comprehend their environment and allow them to make autonomous decisions, collaborate with them, and enhance our capabilities as they grow.

Advantages of Deep Learning

There are several advantages to using Deep Learning:

• Since most Machine Learning algorithms cannot efficiently analyze unstructured data, and these data sets are not utilized.
• Training Deep Learning algorithms can be conducted using different data formats while obtaining insights relevant to the training objective. It is possible, for example, to find relationships between industry analysis, social media chatter, and more to predict an organization’s stock price in the future using Deep Learning algorithms.
• The main feature of using Deep Learning is that it enables a system to carry out features engineering on its own. With this approach, algorithms can scan the data to find the correlated features and combine them to promote faster learning.
• Using Deep Learning, subjective defects can be detected, like minor mistakes on product labels that are hard to train. Furthermore, when consistency becomes a challenge, one can make inspections more robust using Deep Learning.
• Compared to humans, Deep Learning models can perform thousands of routine, repetitive tasks within a relatively short time after being appropriately trained. Further, the quality of the work never drops unless your training data contains irrelevant raw data.

Conclusion:

Given the above and additional benefits of using Deep Learning approaches, it’s evident that Deep Learning will be applied in various high-end technologies in the future, such as Advanced System Architecture and the Internet of Things. Therefore, it is likely that a variety of businesses across the globe will see many more valuable contributions thanks to Deep Learning.