Deep Learning (DL) has become more popular in the Artificial Intelligence (AI) community – it is giving a new shape to global business through the prolific use of autonomous, self-teaching systems, which can build models by directly studying images, text, audio, or video data. Such systems can use these data for future pattern recognition.
According to many technical professionals, businesses can reap the full benefits of AI only when the appropriate levels of competency in extracting reliable business insights are developed in advanced data technologies such as Machine Learning (ML) and Deep Learning. This unilateral opinion among professionals implies that the skills gaps have to be identified and training must be in place to make the best use of available technologies and tools.
What is Deep Learning?
Deep learning is a subset of machine learning that basically mimics the tasks of humans. It involves training the system to understand and duplicate human activities, such as recognizing speech and identifying images. In deep learning the system doesn't need predefined equations; it develops basic parameters from the data and learns its own way to recognize the patterns through layers of processing.
Deep learning has brought forward a better approach to machine learning that produces more efficient results than that of traditional algorithms. The level of accuracy in deep learning techniques is much higher, which is the reason why it is currently receiving so much attention.
This also helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like self-driving cars. Deep learning advancements have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images.
Deep learning surrounds us every day, and this will only increase with time. Whether you are thinking about cars that drive autonomously or even new technologies like parking assistance, traffic control, or face recognition technology at airports. Reports on events are even written more precisely by computers.
ICR is a handwriting recognition system that allows fonts and different styles of handwriting to be taught to a computer during processing to improve accuracy and recognition levels. ICR is an advanced OCR (Optical Character Recognition) technology.
ASR is a technology that develops methodologies to enable the recognition and translation of spoken language into text by computers. It incorporates knowledge and research from various fields like linguistics, computer science, and electrical engineering.
As computer science researchers and data scientists continue to test the capabilities of deep learning, it may appear to be in a research phase for people who are not aware of it. But there are already many practical applications that have affected everyday life and businesses. Popular examples include:
Speech recognition is being used in personal and business capacities more than we realise. Programs like Skype, Siri, and Google Now have integrated deep learning technologies into their systems to recognize voice patterns.
Amazon and Netflix have popularized the notion of a recommendation system with a good chance of knowing what you might be interested in next based on past behavior. Deep learning can be used to enhance recommendations in complex environments such as music interests or clothing preferences across multiple platforms.
There are many applications that can benefit from image recognition, image captioning, and scene description. Law enforcement can use the technology to identify suspects from pictures. It can be a good utility for self-driving cars by using a 360 degree camera.
Neural networks are based on functions of human brains and have been used mainly for processing and analyzing written texts. Machine translation, fighting spam, and information extraction are some of the examples of applications of natural language processing technology.