What is Deep Learning? Top 10 Deep Learning Applications Across Industries

We live in a world where accessibility to technology has made life more accessible than ever. The gift of technology has brought us the reliability of data, knowledge, and information on it. We depend on different technology ways to stay updated or go on and about with our daily lives. The change in technology has bought more than impeccable results for us today.

We are not just dependent but have adopted technological changes in our lives with such ease. The boundaries to where our humanness ends and technology begins have left no boundaries. We are accompanied to live under the comfort and ease of technology. From image recognition to self-driving cars, we have come a long way in this industry. The enhanced peek of the deep-learning base brought us newer scope. Deep learning made it possible for us to imagine a world without any errors and helped us build a world with automation. For people who are not very known with the wonders of machine learning and deep learning, the idea of creating machines has been limited only to automation. If you are wondering what are the wonders of deep learning, then you have come to rescue to the very right place.

What is Deep Learning and its application?

Deep learning is a subdivision of machine learning. Deep learning is inspired by human functioning. It is constructed by and consists of various algorithms. Deep learning is a branch of machine learning, which focuses on building artificial neural networks which practices learning modules on the basis of existing vast quantities of data, and make judgments. It examines, understands, executes, and operates on the basis of pre-existing knowledge. It perspires to be in function inspired by the human brain. Deep learning has gained a lot of popularity with respect to its ability to automatically learn, represent relevant features, and deliver state-of-the-art performance. Deep learning’s potential for automatic learning has been derived from its learning module’s capability to recognize patterns and experience as a practice. The learning practice takes place from unstructured and unlabelled data, unlike traditional machine learning ways. The intention of deep learning is to provide results without any human intervention.

Following are a few examples of the most known and used Deep Learning Applications:

1. Self-Driving Cars:

Self Driving Cars are built on the basic fundamental of the automatic motion of the vehicle. Deep learning has brought the autonomous functioning of automobiles to life. Deep learning is a large set of data set fed into the system with the aim of machine learning and how to act on the basis of the results. Self-driving cars rely on a combination of artificial intelligence, sensors like LiDARs, and RADARs, and computer vision to navigate and make decisions while learning and adapting without any human intervention.

With respect to self-driving cars, it's deep learning involves specific tasks like object recognition, path planning, and decision-making. The functioning of self-driving cars is initiated by data collection through its sensors, cameras, and ultrasonic sensors. These sensors collect data from the surrounding about other vehicles, pedestrians, and obstacles. It is further used by computer vision and deep learning algorithms to interpret it ahead. CNNs (Convolution Neural Networks) deep learning modules are mainly used for object recognition, and for allowing the car to additionally understand the surrounding.

2. Virtual Assistants:

As far as the journey of deep learning’s progress can be tracked, it is said to have begun with virtual assistants. Virtual assistants are based on NLP which is responsible for voice recognition and human interaction experience. The prime example of Virtual assistants can be Alexa by Google, Siri by Apple, and Google Assistant. With the help of a deep learning module, they are made to learn about the command and the accent of the voice by human language evaluation to procure expected results.

The application of Virtual Assistants can also be seen in the translation of speech-to-text and helping break through language barriers. Another use is done under music applications, where the user is recommended songs based on his past music-hearing experience. They are also very useful in auto-responding to specific calls.

3. Visual Recognition:

Visual Recognition has paved its way immensely today. Deep learning application which uses Visual Recognition helps in identifying objects, faces, and places, and segregate with the help of date, time, and event through computer vision. One of the finest examples of visual recognition can be understood through CCTV cameras at traffic signals. With the help of computer vision, we are able to track and capture vehicles even in moving conditions.

Visual Recognition helps has helped us create a digital photo album that divides our photos into different albums of people, events, and time by analyzing and examining with the help of visual recognition. It works on the data fed into the system with the assistance of AI algorithms. Large-format photo convolutional neural networks, Tensorflow, and Python are heavily used to implement visual identification using deep neural networks, which is accelerating growth in this area of digital media management.

4. Healthcare:

Deep learning has significantly revolutionized the growth in the healthcare sector with respect to treatments, patients, medicines, and diagnosis. It has contributed to healthcare by effectively cutting costs and has found its most use in in-depth research of clinical diagnosis and treatment.

Convolutional neural networks through deep learning have made it very feasible to detect tumors, lesions, genomic analysis, MRIs, CT Scans, ECG, X-Rays, etc., and identify medical abnormality and work on its effective diagnosis. These models are even helpful to aid radiologists and pathologists in making more precise detection of medical irregularities.

5. Fraud Detection:

There is no field that has been left untouched by the menace of fraudulent activities. The number of fraudulent activities has increased as digital finance has seen a spike in recent years. But there are new ways adopted by banks, and financial institutions and that is how deep learning came into the picture. Picturing and trying to deliver us fraud-free transactions.

Deep learning in this field examines the user's natural behavior and is built on a long-short-term memory, which helps them analyze the user’s usual experience. It enables deep learning to enable predict and further avoid such fraud activities.

6. Entertainment:

Does it ever intrigue you to understand how the different entertainment applications you operate work on a set of algorithms that recommend songs, movies, and shows in a very personalized way? It is all because of the magic of Deep learning. Deep learning is the tool for personalized marketing today.

As you know, deep learning is a learning module that acts and represents results on the basis of past experiences of the user. In the same way, today we are able to experience personalized marketing by being under the surveillance of such mechanisms. Deep learning models process data accumulated through different sources and gather all of it to extract user information.

7. ChatBots:

ChatBots are built with the integration of NLP (Natural language Processing) with respect to deep learning models which makes them more intelligent, conversational, and capable of providing better user experiences. This is mainly used to provide a human touch to auto-generated replies to the users.

As it is integrated with the NLP, one of the main features of it is to involve sentiment analysis through deep learning modules. It recognizes the tone, sarcasm, and any emotional tone expressed by the user. This helps businesses in understanding the customer satisfaction level.

8. Image Coloring:

Image coloring with the help of deep learning is one of the new advancements we have witnessed today. It involves neural networks to add colors to grayscale images to make them more relevant, life-like, and vibrant. This type of effort was done by hand by a human and today with the help of deep learning.

Today, with high-quality convolutional neural networks we are able to recreate images by giving a specific color to each aspect of the image. With the help of deep learning, we are able to color each and every context of the image.

9. Fraud News Detection:

It has become extremely difficult today to define and evaluate genuine news from fake one. We are today ruled by the empowerment of digitalization and getting influenced by fraudulent news. Hence, deep learning with the help of its convolutional neural networks has helped us filter out news that might come forward as ugly or fraudulent from our feed. This personalization helps us in bifurcating new according to our own preferences, leading to avoiding being influenced by new which might come across as fraud or unpleasant.

10. Deep Dreaming:

Deep Learning networks visualize and enhance features of images. The functioning of a Deep dreaming module interprets to magnify the patterns, color, shapes, and texture of an image.

This is done by passing an image through the network and then figuring out how the gradient of the image relates to the activations of a particular layer. The picture is then changed to emphasize these activations, which enhances the patterns the network perceives and results in a dream-like visual. "Inceptionism" is the name of this approach.

Conclusion:

Deep learning application modules have boomed over the years and knowing unknowingly we have found ourselves bounded by its impact. There is no field left untouched by its progress. Here, I will be concluding our discussion on deep learning applications. If you are intrigued to know better about deep learning you can get the best guidance and training under our IT Training Courses.

Thank you for your patient reading and sticking to the end. I will be waiting for you with the same enthusiasm at the beginning of our next blog.