The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Brief Review on Deep Learning and Types of Implementation for Deep Learning
|
|
Author(s): Uthra Kunathur Thikshaja (Kyungpook National University, South Korea)and Anand Paul (Kyungpook National University, South Korea)
Copyright: 2018
Pages: 13
Source title:
Deep Learning Innovations and Their Convergence With Big Data
Source Author(s)/Editor(s): S. Karthik (SNS College of Technology, Anna University, India), Anand Paul (Kyungpook National University, South Korea)and N. Karthikeyan (Mizan-Tepi University, Ethiopia)
DOI: 10.4018/978-1-5225-3015-2.ch002
Purchase
|
Abstract
In recent years, there's been a resurgence in the field of Artificial Intelligence and deep learning is gaining a lot of attention. Deep learning is a branch of machine learning based on a set of algorithms that can be used to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations. Estimation of depth in a Neural Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. This chapter describes the motivations for deep architecture, problem with large networks, the need for deep architecture and new implementation techniques for deep learning. At the end, there is also an algorithm to implement the deep architecture using the recursive nature of functions and transforming them to get the desired output.
Related Content
|
Kamel Mouloudj, Vu Lan Oanh LE, Achouak Bouarar, Ahmed Chemseddine Bouarar, Dachel Martínez Asanza, Mayuri Srivastava.
© 2024.
20 pages.
|
|
José Eduardo Aleixo, José Luís Reis, Sandrina Francisca Teixeira, Ana Pinto de Lima.
© 2024.
52 pages.
|
|
Jorge Figueiredo, Isabel Oliveira, Sérgio Silva, Margarida Pocinho, António Cardoso, Manuel Pereira.
© 2024.
24 pages.
|
|
Fatih Pinarbasi.
© 2024.
20 pages.
|
|
Stavros Kaperonis.
© 2024.
25 pages.
|
|
Thomas Rui Mendes, Ana Cristina Antunes.
© 2024.
24 pages.
|
|
Nuno Geada.
© 2024.
12 pages.
|
|
|