An Introduction to Neural Networks and Deep Learning

Research output: Chapter in Book/Report/Conference proceedingChapter

40 Citations (Scopus)


Artificial neural networks, conceptually and structurally inspired by neural systems, are of great interest along with deep learning, thanks to their great successes in various fields including medical imaging analysis. In this chapter, we describe the fundamental concepts and ideas of (deep) neural networks and explain algorithmic advances to learn network parameters efficiently by avoiding overfitting. Specifically, this chapter focuses on introducing (i) feed-forward neural networks, (ii) gradient descent-based parameter optimization algorithms, (iii) different types of deep models, (iv) technical tricks for fast and robust training of deep models, and (v) open source deep learning frameworks for quick practice.

Original languageEnglish
Title of host publicationDeep Learning for Medical Image Analysis
PublisherElsevier Inc.
Number of pages22
ISBN (Electronic)9780128104095
ISBN (Print)9780128104088
Publication statusPublished - 2017 Jan 30


  • Convolutional neural network
  • Deep Boltzmann machine
  • Deep belief network
  • Deep learning
  • Neural networks

ASJC Scopus subject areas

  • Engineering(all)


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