WHAT IS DEEP LEARNING?
Deep Learning is an artificial intelligence algorithm which try to mimic the workings of the human brain in processing data and creating patterns for decision making. Deep learning is a part of machine learning in Artificial Intelligence (AI) that has networks, capable of learning unsupervised from data that is unstructured or unlabeled, as well as doing the supervised learning with labeled data. Deep Learning is also known as Deep Neural Learning or Deep Neural Network.
Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open source platforms with consumer recommendation apps, and medical research tools that explore the possibility of reusing drugs and CAD systems are a few of the examples of deep learning applications.
WHAT IS OUR GROUP RESEARCHING ON?
DEEP LEARNING IN INTELLIGENT VEHICLES AND TRANSPORTATION
The main application of deep learning within the automotive domain is that of advanced computer vision and perception. Visual tasks, including, but not limited to lane detection, pedestrian detection, road signs recognition and blind-spot monitoring are handled more effectively with deep learning.
DEEP LEARNING IN MEDICAL AND HEALTH
Deep Learning is transforming the world of medicine. It can help doctors make faster, more accurate diagnoses. It can predict the risk of a disease in time to prevent it. It can help researchers understand how genetic variations lead to disease. It enhances doctors’ ability to analyze medical images. It’s advancing the future of personalized medicine.
DEEP LEARNING IN AGRICULTURE
Deep learning algorithms can take a decade of field data—insights about how crops have performed in various climates and inherited certain characteristics—and use this data to develop a probability model. With all this information, far more than any single human can grasp, deep learning can predict which genes will most likely contribute a beneficial trait to a plant.
DEEP LEARNING IN INDUSTRIAL AUTOMATION
Preventative maintenance/repair, condition monitoring (machine efficiency), and optimizing supply chains can all be had with integrated deep-learning algorithms. Manufacturers are also starting to integrate AI programs into their automation processes and the inclusion of other advanced technologies (IoT, modular/adaptive hardware, distributed intelligence, etc.), is helping manufacturers gain a foothold in industry revolution.
DEEP LEARNING IN ANOMALY DETECTION & CYBER SECURITY
Deep Learning is not only expected to develop more accurate models for detecting fraud, network intrusion, cyber attacks and other anomalies, but it also learns from new data on massively parallel hardware
DEEP LEARNING IN RECOMMENDER SYSTEMS
Recommender systems influence us in daily basis, from internet search to movie suggestions to online shopping. Deep Learning attempts to show us the information and products we want, based on user trends and past behavior.
DEEP LEARNING IN IOT AND BIG DATA
Deep learning is becoming have an important role in IoT and Big Data analytics. Devices are sparse with different conditions. Monitoring sensor data continuously is also cumbersome and expensive. Deep learning algorithms can help to solve these risks. Deep Learning algorithms allows the developer to concentrate on better things without worrying about training them in manual fashion.
DEEP LEARNING IN ENTERPRISE
Deep Learning adoption in the enterprise is expected over the next few years, initially to improve existing processes and then reinvent them. Applications that could benefit from Deep Learning include smart sensors and smarter appliances in IoT, as well as automation across operational technology, information technology, customer support, and enterprise workflows.
