Deep learning can be defined as the process of machine learning application where computers are taught to do things that originally only come naturally to humans. Deep Learning Applications are the various ways that deep learning can be applied. So deep learning is quintessentially teaching machines to learn by example. So an example of Deep Learning Applications will be training a machine learning program to learn the colors of objects and in that way, the machine will be able to paint a black and white picture based on past learning and information of colored photographs it has acquired. Deep learning allows a computer model to learn to perform classification tasks directly from images, text, or sound.
Below we discuss some fun and interesting Deep Learning Applications and ways that deep learning has become present in our everyday lives
Deep Learning Applications in Healthcare
Deep learning has so many possible applications in healthcare from helping with diagnosis to predictive medicine and patient monitoring. One of the applications in medicine that has been brought about by deep learning has been advancements in precision medicine and population health management. Deep learning teaches computers to be able to make detection and use quantitative imaging, and other decision support tools. The future of deep learning in healthcare is bright as there are so many possible applications. More and more deep learning and technology innovations will continue to pop up in the healthcare industry.
The buzz about self-driving cars has been around for a while now. The technology behind it is deep learning. It’s already been applied through driver-assistance services being installed in cars. Self-driving cars will be able to use its digital sensory system which mimics that of humans to safely move a car from one location to the next. The technology works by repetition and constant learning. Feeding the program information in different ways to the point that it understands all possible variations. The possibility of having self-driving cars in circulation for the entire population to purpose is slowly becoming a reality as there have already been prototypes made like the Google Self-Driving Cars.
Voice search is the future and many website owners are being told to optimize for voice search if they want any chance of performing well in SEO. This is because voice search technology which is powered by deep learning is starting to gain more and more popularity. Voice-activated devices like Alexa and Google Home are becoming more popular and Apple’s Siri has been around for a while now. A new entry into the field is Microsoft’s Cortana.
Image recognition is one common use of deep learning applications. With image recognition, people and objects can be identified and then named after the deep learning software understands the nature of the image. Deep learning powered image recognition is used commonly on social media sites especially Facebook. Facebook learns your face and thus can automatically identify you in photos. It is also able to identify other objects and provide an alt description of the objects and possibly even tag them.
Machine Learning Language Translations
Machine learning translation is one of the top language application modes of deep learning. Deep learning does automatic text translation and automatic image translation. It can automatically translate text or voice commands or words and sentences into different languages. A Common deep learning application for language translation would be Google Translate. It can also generate text from learning how to spell and punctuate and how to capture a style of writing. An example of this will be the Google Grammarly Writing assistant. Deep learning uses large neural networks which are recurrent to learn the relationship between items in the sequences of input strings and then generate text.
One fun and maybe scary use of deep learning technology is in generation sounds for otherwise silent videos. It is used a lot to add sound to silent movies. The deep learning system is extensively trained using lots of videos examples which it absorbs. Then it is given a silent video which it analyzes using its database of different sounds it has absorbed to come up with the best matches of what the people are saying in the video. Thus with this machine learning can be used to automatically add sounds to silent videos.
Just like deep learning technology can be trained to learn sounds, it can also be trained to understand. This means the machine can create new samples of handwriting, read handwritings and even come up with new handwriting in which case they will be called fonts.
This is an extension of deep learning application in image recognition. Deep learning technology can be used to create captions for images. The caption will correctly and succinctly describe the contents of the image. This happens because deep learning technology would have mastered object recognition. The technology identifies all the objects in the photograph or image and then labels them and then comes up with a natural language that summarizes all the objects and how it translates in the photograph.
Adding Color to the Images
As mentioned briefly in the introduction, one deep learning application is adding color to black and white images. From learning about objects and their context in their natural colored state it can correctly color a black and white photograph. This deep learning application is quite a complicated process as the approach involves the use of very large convolutional neural networks and supervised layers that recreate the image with the addition of color.