The world of AI, ML and Deep learning continues to evolve and expand. With the significant rise in its application of Deep Learning and allied technologies, across the business spectrum, it has laid the foundation stone for a new future.
From automated cars, to robots being your friend, these are no more a part of fictional story, they are here and are transforming our lives. The growth in Deep Learning applications in the real world will boost its market. Hence, it is expected to witness a CAGR of 33.5% between 2023 to 2030.
The triggering factors contributing to the growth of Deep Learning and allied technologies include:
- Advancements in data center capabilities
- High computing power
- Ability to perform tasks without relying on human input
- Rapid adoption of cloud-based technology
While the Deep Learning market is abuzz with new developments , every day. In this blog, we have hand-picked some unconventional yet outstanding Deep learning applications examples.
What is Deep Learning?
Deep Learning is a subset of Machine Learning where neural networks have a significant role. The Deep Learning algorithms are designed and developed akin to the human brain. Their focus is to establish relationships in datasets. The Deep Learning algorithms enable computers to identify trends and patterns, it also solves complex problems of ML and AI.
It makes use of artificial neural networks (ANN) to find the hidden patterns that unfold connections between various variables present in a dataset. The neural networks are trained on sample data, and the insights are used to replicate applications on new datasets. In the next segment, we are going to highlight the real-life applications of Deep Learning.
8 Amazing Deep Learning Applications in Real World:
As we have mentioned above, the Deep Learning domain is expanding, and we are witnessing a massive growth in the applications of Deep Learning. Its prevalence is not juts limited to the tech industry, but our day-to-day life is also exemplifying the Deep Learning use cases.
1. Image Recognition/Visual Recognition
There has been a significant development in Deep Learning algorithms that have resulted in accurate results. Image recognition or visual recognition is one such application that is used in accurate object detection and facial recognition. The neural networks are designed to recognize patterns. It is divided into three layers: input layer, hidden layer and output layer.
While the input layer receives the signal, the hidden layer is responsible for processing and the outer layer reveals the outcomes Each of these network is connected via nodes that are responsible for the computation. One of the examples is removing noise from images using the pre trained neural network. The latter identifies and removes artifacts from images.
2. Natural Language Processing (NLP)
Some of the common Deep Learning applications in NLP are in sentimental analysis, language translation , speech recognition and chatbots. Some of the common examples of this are Siri and Alexa. They use Deep Learning technology to understand and respond to human language.
3. Autonomous Vehicles
Self-driven vehicles or autonomous vehicles are no more a dream. It plays a critical role in enabling self0driving cars to perceive the circumstances and drive the car accordingly. With the help of Deep Learning technology, autonomous vehicles can detect lane, identify traffic congestion, optimize routes and predict pedestrian behavior.
4. Healthcare
The widening horizons of Deep Learning are now encompassing the healthcare sector as well. The modern day medical and healthcare sector majorly rely on advanced technologies like AI and Deep learning for diagnosis, assessment and analysis of patient’s data. All this eventually helps in making a more precise and informed decision. Some of the applications of Deep Learning in healthcare are diagnosis of cancer from mammograms or detecting abnormalities in MRI scans. This technology can also be used to personalize medicine and aid the process of drug discovery.
5. Recommender Systems
E-commerce companies and OTT platforms like Netflix have actively been using Deep Learning algorithms to identify the preferences and behaviour of their few words. This eventually helps them in providing personalized recommendations of films or music and improves the overall customer experience.
6. Robotics
Robotics is yet another application of Deep Learning in AI. The industrial robots have long been used across the industrial spectrum for simplifying tasks like assembly line, welding and others. Deep Learning enables robots to understand the surrounding, recognize the object and perform the task accordingly.
7. Fraud Detection
Deep Learning application of Deep Learning in fraud detection has gained traction in recent times. With the help of Deep Learning models, it becomes easier to identify patterns and anomalies in the financial transactions that enable faster fraud and theft detection.
8. Virtual Assistants
This technology powers intelligent virtual assistants like Google Assistant, Siri, and Cortana. These Vas are able to comprehend and respond to the voice commands, thus providing a more personalized assistance.
Summary of Applications of Deep Learning:
| Application | Overview |
| Computer Vision | Deep Learning can be used for image recognition, image segmentation |
| NLP | Speech Translation, Sentiment Analysis, Chatbot |
| Autonomous Vehicle | Self-driven vehicles rely on Deep Learning technology to perceive the status of traffic and decide its course of movements |
| Healthcare | Image analysis, disease diagnosis and drug discovery |
| Recommendation System | With the help of Deep Learning algorithm, companies like Netflix can assess consumer preferences to recommend preferred movies. |
| Robotics | Robotic application like grasping, navigation can be run smoothly and flawlessly using Deep Learning algorithm |
| Fraud Detection | Banking and e-commerce companies can analyze patterns and anomalies in transaction of data. |
| Voice Assistant | Siri, Alexa, Google Assistant operate on NLP |
| Gaming | Deep Learning algorithm can be to improve gaming experience by creating a realistic and simulative experience. |
Wrapping it up !!!
These are some of the common applications of Deep Learning in AI. With the growing penetration of these technologies in our lives, there has been a significant rise in the demand for professionals who can seamlessly execute this technology using the best Machine Learning frameworks. In the times to come there will be a significant rise in the working of Deep Learning algorithms. They will be more precise and provide accurate outcomes. Hence this will also open the avenues of job for individuals having domain. If you do or are willing to be a Deep Learning expert, and are looking forward for making a career in this domain, then connect with Pickl.AI today.
