Deep Learning is also known as deep structured learning and is a subset of machine learning methods based on learning data representations, concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
Applications of Deep Learning:
- Colorization of Black and White Images
- Adding Sounds in Silent Films
- Machine-assisted auto translation
- Object Classification in Photographs
- Automatic Handwriting Generation
- Character Text Generation
Automatic Colorization of Black and White Images
In image colorization, we face problems in adding colour to black and white photographs. Earlier, this was done manually, which involved a lot of effort and scope of error.
Deep learning is helpful in identifying the objects and their context within the photograph to throw some colours in, much like how a human operator would approach the issue. This capability of deep learning yields high quality and very large convolutional neural networks trained for Image Net and the best choice for the problem of image colorization.
Generally very large artificial neural networks and supervised layers that recreate the image with the addition of colour. In the same way these can be used to colorize still frames of black and white movies.
Dubbing Silent Films
With the help of deep learning, a computer can be used to dub silent movies. A deep learning model combines the video frames with a database of pre-rerecorded sounds in order to select a sound to play that best matches what is happening in the scene.
Then, the system needs to create a test environment, where humans determine which video had the real or the fake sounds.
Automatic Machine Translation
Another astonishing feature of Deep Learning is that it can automatically translate given words, phrase or sentence from one language, to another language. However, this is not new as automatic machine translation has been around for a long time, but deep learning specializes in two specific areas, which are Automatic Translation of text and images.
Identification and Recognition in Photographs
Deep learning helps in classification of objects within a photograph as one of a set of previously known objects.
A more complex variation of this task called object detection involves specifically identifying one or more objects within the scene of the photograph and drawing a box around them.
In this case, deep learning helps to generate new handwriting for a given word or phrase.
When the handwriting samples were created, the handwriting is provided as a sequence of coordinates used by a pen. With this, a pattern of pen movements and letters is learned and new examples can be generated ad hoc.
Automatic Text Generation
In this model new text is generated, word-by-word or character-by-character. It is capable of learning how to spell, punctuate, form sentences and even capture the style of text in the core. Large amount of neural networks are used to learn the relationship between items in the sequences of input strings and then generate text.
In this post we saw only 6 applications of deep learning that intended inspire us. Deep learning application is not only limited to above examples but also applicable in image caption generation, automatic game playing and more.
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