Author:
Vakalopoulou Maria,Christodoulidis Stergios,Burgos Ninon,Colliot Olivier,Lepetit Vincent
Abstract
AbstractDeep learning belongs to the broader family of machine learning methods and currently provides state-of-the-art performance in a variety of fields, including medical applications. Deep learning architectures can be categorized into different groups depending on their components. However, most of them share similar modules and mathematical formulations. In this chapter, the basic concepts of deep learning will be presented to provide a better understanding of these powerful and broadly used algorithms. The analysis is structured around the main components of deep learning architectures, focusing on convolutional neural networks and autoencoders.
Reference74 articles.
1. Rosenblatt F (1957) The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory, Buffalo
2. Minsky M, Papert S (1969) Perceptron: an introduction to computational geometry. MIT Press, Cambridge, MA
3. Minsky ML, Papert SA (1988) Perceptrons: expanded edition. MIT Press, Cambridge, MA
4. Linnainmaa S (1976) Taylor expansion of the accumulated rounding error. BIT Numer Math 16(2):146–160
5. Werbos PJ (1982) Applications of advances in nonlinear sensitivity analysis. In: System modeling and optimization. Springer, Berlin, pp 762–770
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献