A relatively new area of computer science that has shown tremendous growth in recent years is deep learning artificial intelligence (AI). Deep Learning algorithms are being used to solve many issues, from self-driving cars to facial recognition software. There are countless possible applications. This blog article will examine some current uses of deep learning in the real world.
What is Deep Learning?
Deep learning is a subclass of machine learning, and a neural network is a network with three or more layers. These neural networks attempt to mimic the function of the human brain and allow it to “learn” from enormous amounts of data, even though they cannot match it in any way. More hidden layers can assist refine and optimizing for accuracy, even if a neural network with only one layer can still create approximations.
What are the applications of Deep Learning in real life?
Depending on the problem that needs to be solved, a variety of deep learning methods can be used. Several examples include:
Self-driving cars
Convolutional neural network-based supervised machine learning models are used in self-driving cars (CNNs).
Essentially, this entails guiding them through the process of recognizing objects, such as traffic signs or other vehicles, using camera images given into these computer programs as they drive around rather than explicit programming instructions.
Natural Language Processing (NLP) and Speech Recognition
Deep learning can also be used for speech recognition and natural language processing, allowing robots to comprehend communication similar to humans.
This type of deep network, which has been trained on enormous datasets of annotated text or audio data to replicate human speech or writing, usually uses convolutional neural networks and long short term memory (LSTM) recurrent neural networks.
Computer Vision
Computer vision uses deep learning to recognize objects and actions in a visual scene.
This is applicable, for instance, to social robotics, where a robot may learn how to assist around the house by recognizing commonplace objects like plates or clothes while independently exploring its surroundings.
Machine Translation
Machine translation technologies automatically translate material from one language into another without requiring humans to manually input translated words or phrases beforehand and also use deep learning techniques.
Medical Image Analysis
Deep learning is also being used in medical imaging to identify mental diseases, detect cancer in mammograms and other radiological images, and forecast cardiovascular risks.
Video Games
Recent advances in deep learning have enabled computers to teach themselves how to play video games by merely watching the screen. In contrast, it employs reinforcement learning techniques when playing against another player and succeeds at something every time.
It is rewarded with a signal. However, every time runs against a roadblock. Over time, the computer learns to navigate obstacles independently, without preprogrammed guidance.
Big Data and Data Mining
Big data and mining applications also utilize deep learning, where computer systems may discover hidden patterns in enormous datasets. These patterns may range from predicting what movies a user will want to watch next based on previous viewing behavior to spotting credit card fraud.
Online Self-Service Solutions
Deep learning is also applied to self-service technologies, which help organizations grow by enabling clients to do tasks independently rather than requiring human support.
Real-time Predictive Analytics
Deep learning algorithms are also starting to be used in real-time predictive analytics tasks, including avoiding traffic congestion, determining the best routes or itineraries based on the situation, and foreseeing future issues before they occur.
Computer Vision and Classification Tasks
Classification tasks involve computer algorithms that can understand what each thing means based on samples of the same object or concept, which are the major area where deep learning is currently employed.
Deep learning is used in computer vision to determine objects and activities in a visual scene.
This is applicable, for instance, to social robotics, where a robot may learn how to assist around the house by recognizing commonplace objects like plates or clothes while independently exploring its surroundings.
Face Detection and Recognition
An excellent example is face detection and recognition, which can now be done quite accurately with only one image, instead of hundreds or even thousands, before it can recognize something correctly.
Even though some researchers have highlighted worries about how this information can occasionally be misused, this approach has also been used to find nudity in images and identify offensive content for social media posts.
Object Detection & Identification Algorithms
Computer systems can now distinguish specific items inside photos and locate their locations, whether they are fixed or moving, thanks to advancements in object recognition and identification algorithms.
The same technology is utilized by robots that can navigate a warehouse without bumping into anything and self-driving cars that need this data to operate on the road.
Conclusion
The practical thing about deep learning is that it can be used in many different areas, which implies that this kind of artificial intelligence has many uses.
These networks process a huge amount of data, break it down into digestible bits, and compare new information to what they’ve already learned. Consequently, they provide remarkably accurate results with hardly any human involvement. Visit ONPASSIVE to learn more.