Deep Learning Applications That Changed The World
Deep Learning is a technology that makes machines capable of making decisions like human beings. It is a growing field which helps in every sector like Real Estate, Banking, Healthcare, Stock Market, Self Driving Vehicles, etc and many more areas.
The world is slowly turning into a universally connected sphere. With the power of the cloud, internet, and connected apps, the limitations that existed earlier are slowly being reconstructed – and a big part of this reason is Deep Learning(Neural Network). Deep Learning is responsible for many changes in the world today, a majority of which have far-reaching implications on the way we live in the world.
Nowadays Companies/organisations are deploying Deep Learning to improve their different products and services and no other company has invested that much in deep learning than Google. A couple of years ago Google Deep Learning Neural Network was shown 10 million unlabeled images from YouTube and it proved that its accuracy is twice for identifying the objects in the images (cats, human faces, flowers, various species of fish, and thousands of others) as compared to the previous method. One other scenario is when Google deploys Deep Learning Neural Network for Android voice search the error dropped 25% overnight.
Some Popular application of Deep Learning is -:
- Fake news detection
- Satellite Imagery
- Object Detection
- House Recommendation Search Engine
- Text to code
- Multiple Use Cases in the Entertainment Industry
- Healthcare – Malaria cell Detection
These are some popular use-cases with applications in many more fields:
Fake News Detection –
In today’s world, fake news can be found everywhere on social media platforms and the internet. Fake news is present in the form of images, tweets, and news. To detect fake images we use the Deep Learning model and metadata analyzer to detect whether it is fake or not. To detect it we pass the image to the deep Convolutional neural network which checks them pixel by pixel for any pixel that has been tampered with. Another way to check fake images is using Deep Fake. To detect fake news or fake tweets we use LSTM models to detect fake news, fake tweets from the tweets.
Satellite Imagery –
As we know, satellites revolve around the earth and take pictures of the earth at certain intervals of time. Satellites have different sensors that allow them to record spectrums that human eyes cannot see. Any image taken by the satellite can have 12 or more layers, and each layer brings more information. By combining the layers, you can create indicators that will give you additional insight into what is happening on the ground.
Object Detection –
Object detection helps in detecting objects. There are some popular use cases like it is used by AWS to detect faces, objects, text, and many more things from an image or video. AWS provides a deep learning service that is amazon recognition which is an object detection with transfer learning, and It has different features like it can detect text from an image, can detect faces. Another example is a few years back in the UK, where there was a royal marriage going on and Amazon fed the live feed of it into amazon’s recognition algorithm, detecting how many or which celebrities attended the wedding. This is also used in self-driving cars to detect objects around roads.
Multiple Use Cases in the Entertainment Industry-
Deep Learning doesn’t just have a major impact in the legal or the IT(Information) industry, but it also plays a very important role in the entertainment industry nowadays, keeping millions of people on the hook for another minute every day. And few of the applications are truly amazing.
While it might seem intuitive to think that Netflix provides a personalised user experience to its customers through the use of Deep Learning (and so does Amazon).
Healthcare – Malaria cell Detection –
Perhaps another use of Deep Learning has far-reaching and multiple implications when it comes to the healthcare industry. The entire industry, just like all industries that have been impacted by these emerging Deep Learning technologies, is going through a transformation – and GPU computing is driving a lot of it forward. It is reducing time, effort, and cost. One of the popular use cases is malaria cell detection in which we pass a malaria cell image to the deep convolutional neural network and it will predict whether the person has malaria or not. There are several more examples that are more accurate, and it also reduces cost and effort.
So from the above instances, we can conclude that Deep learning is changing life a lot. It provides us many solutions and it helps different industries in growing their businesses or making the life of people easier and easier. Deep Learning also reduces our effort, time, and cost.