Open AccessFeature Analysis Detection Algorithms-Review Paper
Author(s) -
Sheetal Mahadik,
Namrata J. Ravat,
Kunal Singh,
Suvita D. Yadav
Publication year - 2020
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-645
Subject(s) - artificial neural network , computer science , task (project management) , artificial intelligence , process (computing) , feature (linguistics) , machine learning , point (geometry) , deep learning , time delay neural network , deep neural networks , engineering , linguistics , philosophy , geometry , mathematics , systems engineering , operating system
Back within the 1950s, Minsky and McCarthy described AI as any task performed by a program or a machine that, if a person administered an equivalent activity, we can say that he had to use his intelligence to accomplish the task. Neural networks help us to train and process Machines. These are brain-inspired networks of interconnected layers, called neurons, that feed data into one another. During the training of those neural networks, the weights attached to different inputs will still be varied until the output from the neural network is extremely on the brink of what is desired, at which point the network will have 'learned' the way to perform a specific task. A subset of machine learning is deep learning, where neural networks are expanded into sprawling networks with an enormous number of layers that are trained using massive amounts of knowledge. It is these deep neural networks that have fuelled the present breakthrough within the ability of computers to hold out tasks like speech recognition and computer vision.