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Deep Learning Methods and Applications

  • Jamil AhmadEmail author
  • Haleem Farman
  • Zahoor Jan
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

This chapter introduces the various methods existing beneath the umbrella of deep learning paradigm, their intricate details, and their applications in various fields. Deep learning has substantially improved the predictive capacity of computing devices, due to the availability of big data, with the help of superior learning algorithms. It has made it possible as well as practical to integrate machine learning with sophisticated applications including image recognition, object detection, self-driving cars, drug discovery, and disease detection. The superior and reliable performance of deep learning methods has attracted the attention of researchers working in every field of science to utilize their strengths in order to solve problems. In addition to that, the knowledge reuse in deep learning is an interesting aspect of this technology which will also be discussed.

List of Acronyms

CNN

Convolutional neural network

DNN

Deep neural networks

RNN

Recurrent neural network

LSTM

Long short-term memory

IR

Information retrieval

BoW

Bag-of-words

CBIR

Content-based image retrieval

NLP

Natural language processing

ML

Machine learning

MTL

Multitask learning

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Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceIslamia College PeshawarPeshawarPakistan

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