The application of Deep Learning has outperformed the conventional processes ranging from natural language processing and computer vision. Whether in academics to study intelligence or in the industry of intelligent systems, Deep Learning is heavily used for various tasks.
This nascent technology is percolating into smartphones, driving advancements in healthcare, creating efficiencies in the power grid, improving agricultural yields, and many more. The versatility of deep learning is highlighted more in the past few years with the experiments of Microsoft Tay, Facebook M, and Google’s DeepMind AlphaGo. Here are few most amazing and inspiring applications of Deep Learning –
1. Colorization of Black and White Images
Deep Learning tends to learn the patterns that naturally occur in photographs. For example – the sky is blue, the grass is green and rose is red. “Let There be Color!” is a system which restores colors in black & white photos. Though it sometimes makes mistakes and hence needs some human interventions.
2. Pixel Restoration
In early 2017, by training Deep Learning Network, Google Brain researchers took a low resolutions photo of human faces and predicted what each of them may really look like. This method of artificially enlarging a low-resolution photograph to recover a plausible high-resolution version is termed as Pixel Recursive Super Resolution.
3. Real-time Multi-person Pose Estimation
Deep Learning helps animators in estimating the poses of the characters or people, that too in real time. You can see video below to get a glimpse, how it works. The network knows where these dancing people are and how they move. No devices were used directly on them, this is solely done only be analyzing the video.
4. Describing Photos
Deep Learning has taught a computer to automatically classify photos. It is the technology behind auto-tagging in Facebook and auto labeling of your photos by Google for an easier search.
Not only labels and Deep Learning allows taking it several steps forward by describing all the various elements in a photo. In an experiment by Andrej Karpathy and Li Fei-Fei, the computer system not only to learned to classify the elements of a photo but also to describe them with English Grammar.
5. Real-time Analysis of Behaviors
As they can estimate people poses, Deep Learning networks can also describe and recognize photos. DeepGlint is one such solution that uses the technology Deep Learning to get real-time insights into the behavior of people or cars et cetera. This application of Deep Learning is still on the sketchy side, but it is worth being familiar with.
6. Generating Photos of Galaxies
Deep Learning has made things easy for Scientists. To study about a terrestrial or extraterrestrial object to study the natural world and universe. Astronomers and Scientists can study the photos of galaxies and volcanoes created by Deep Learning just as the way they are.
Google Translate app automatically translates images with text to a language of your choice. How do they do that? It is done with the help of DL. You just need to hold the camera on top of the object and your phone with a little bit of help of deep learning network reads the image or OCR it (which is to convert the image to text) and then translate it. With this innovative technique, languages will no more be a barrier and the entire world will be able to communicate with each other in a hassle-free way.
8. Estimate Solar Savings Potential
Projects like Google Sunroof uses Deep Learning neural networks to separate your roof from its surroundings. It uses aerial photos from Google Earth to create a three dimensional model of your roof. With the help of sun’s trajectory and weather patterns, it predicts how many solar panels you need to create energy. Try it right now with Google Sunroof.
9. Self-Driving Cars
You must have heard about this concept a lot. It is time when you can actually see it in action. In this video, a Tesla electric vehicle drives without human intervention. You can analyze how it distinguishes roads, road signs, people crossing the road and different type of objects.
Robotics and Deep Learning always go hand in hand. Robotics faces many unique challenges when robotic platforms move out of the lab and go into the real world. Deep learning algorithms are general non-linear models which helps robotics learn features directly from data, making them an excellent choice for robotics applications.
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