Deep Learning Network: Deep Neural Networks

Deep Learning Network: Deep Neural Networks

Bhanu Chander (Pondicherry University, India)
Copyright: © 2020 |Pages: 30
DOI: 10.4018/978-1-7998-1159-6.ch001

Abstract

Artificial intelligence (AI) is defined as a machine that can do everything a human being can do and produce better results. Means AI enlightening that data can produce a solution for its own results. Inside the AI ellipsoidal, Machine learning (ML) has a wide variety of algorithms produce more accurate results. As a result of technology, improvement increasing amounts of data are available. But with ML and AI, it is very difficult to extract such high-level, abstract features from raw data, moreover hard to know what feature should be extracted. Finally, we now have deep learning; these algorithms are modeled based on how human brains process the data. Deep learning is a particular kind of machine learning that provides flexibility and great power, with its attempts to learn in multiple levels of representation with the operations of multiple layers. Deep learning brief overview, platforms, Models, Autoencoders, CNN, RNN, and Appliances are described appropriately. Deep learning will have many more successes in the near future because it requires very little engineering by hand.
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Background

Artificial intelligence (AI) is defined as a machine has the ability to doing everything a human being can do and produce better results. Means AI enlightening that data can produce a solution for its own results. Inside the AI ellipsoidal, Machine learning (ML) has a wide variety of algorithms produce more accurate results. In fact, ML is a branch of statistics whereby the algorithms learn from the data as it is input into the system. Machine learning from last two decades stands as one of the greatest development in information technology. As a result of technology improvement constantly increasing amounts of data is available, so there is a need for a good reason to believe and smart data analysis becomes a necessary ingredient for technological progress. Machine learning methods give a boost to many aspects of modern society from content filtering to object recognition which growing interestingly in modern computers, Smartphone’s. Performance of machine learning technique highly depending on the representation of data given to them. Many AI, ML techniques solve tasks by designing feature sets which examining and extracting useful information which designed meaning full for that task, but it is very difficult to extract such high level, abstract features from raw data moreover hard to know what feature should be extracted.

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