Deep learning is an aspect of machine learning used to train computers to learn by example. Machines process and filter complex information to produce output. We see deep learning everywhere. Self-driving cars, virtual assistants, and visual recognition are some notable applications of deep learning across industries.
Let us dig deeper into the applications of deep learning across industries.
Computer Vision
Computer vision is an aspect of AI that enables computers and systems to make meaning from digital images and other visual inputs. The computers then go ahead to make recommendations based on them. It works similar to human vision, but humans have the advantage. The human sight has the advantage of context to tell how far apart objects are. As a result, computer vision trains machines to identify objects and know their distance. However, it has to do this within a short time with the help of cameras and algorithms.
Computer vision requires a lot of data. It analyses the data until it identifies distinctions and recognizes the images. Most commercial AI vision systems are very specialize and develop in order to attain their visual inspection, remote monitoring, and so on. The aim of this is to increase the operational efficiency area. We discover that deep learning models can autonomously analyze video streams of almost any sensor.
Natural Language Processing (NLP)
Natural language refers to the way human beings communicate with each other. So, we communicate typically in speech and text. Consider how much text we see on a daily basis. We pass by billboards, and read email signs, road signs, and notification messages; these are all text. When we speak about speech and the way we interact when we speak to one another, it is easier to learn to speak sometimes than to write. Natural language processing is closely link to linguistics. Serial linguistics is the scientific study of language that includes grammar, semantics, phonetics, and related terms.
Machine learning practitioners are interested in working with text data. They are concerned with the tools and methods from the field of NLP. Some people have described the field of NLP as linguistic science permitting a discussion of both classical musicians and statistical methods.
Benefits of Natural Language Processing (NLP)
Furthermore, it is correct to describe natural language processing as the ability of programs to understand human language as it is spoken and written. It helps computers to understand the language as humans do. Input is change into code that the computer can understand. Just as humans utilize our brain to process information, computers process input with stored programs. There is a process of data processing that helps to clean the text data so that it is easy for the machines to analyze it correctly. Pre-processing presents data in a workable form and makes it easier for the algorithm to work with.
Natural language processing NLP is another segment of deep learning. It combines artificial intelligence with human language. Due to the nuances and intricacies of language, this aspect is one of the most complex and difficult deep learning algorithms to create. For instance, one word can have different meanings in different languages.
NLP is used to understand the structure and meaning of language. It analyses different aspects such as syntax, semantics, and so on. Then these are translated into rule-based algorithms that can solve specific problems. For example, in Gmail, an email sent is categorize into promotions or primary as a result of email filters that inspect the content and associate the tags into which category the email belongs.
