Deep Learning (DL) is a term in data science that has become a hot word in the technological world, business and marketing. DL is often confused with Machine Learning (ML). The difference is that deep learning is a part of machine learning, an advanced technique for learning algorithms for making independent decisions, that is a part of the wider field of artificial intelligence.
DL is a set of ML methods that structure methods into layers. They are inspired by bio-neural networks and enable computers to develop their “thinking” for making more independent conclusions.
Deep Learning methods can leverage ML and its outstanding qualities to the next level:
Deep Learning using huge neural networks is a learning machine for automating tasks performed by human visual systems. DL provides a very accurate classification of images, as well as object detection, image recovery and image segmentation. Even handwritten digits can be recognized.
The Google Translate app uses Deep Learning technology for visual translation. It uses the deepest neural networks to determine the word while scanning images. The DL design determines whether there are letters in the illustration. When the letters are assigned and the text is recognized, the application translates the title from the image, into your native language.
Thanks to Deep Learning, machines have every chance not only to chat but also to perceive what you are saying. A good case is the LipNet system, created with the introduction of the technology of neural networks by scientists from the University of Oxford. LipNet became the first system in the world capable of recognizing speech by the lips not only by individual words but also immediately by entire sentences. For this, the system processes the video sequence, dividing it into many fragments and layers.
The system synthesizes sounds to correspond to a silent video. The system is taught , using 1000 examples of video with the sound of a drum stick hitting on various surfaces and creating various sounds. The model of the DL binds video to the database of the pre-recorded sounds, choosing the most similar one to the stage sound.
Machines study punctuation, grammar and style of fragments of text and this uses the model worked out for automatic creation of brand-new text with correct orthography, grammar and style.
A new handwriting is created using the examples of the handwritten input. Handwriting is given as a sequence of coordinates, used by the pen for creation of the sample of handwriting. From this, the relationship between the pen movement and the letters is learned and new examples can be created.