Training artificial neurons: an introduction to machine learning

Joshua A. Goldwag; Ge Wang

doi:10.1117/12.2530718

10 September 2019 Training artificial neurons: an introduction to machine learning

Joshua A. Goldwag, Ge Wang

Proceedings Volume 11113, Developments in X-Ray Tomography XII; 111131P (2019) https://doi.org/10.1117/12.2530718
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

Machine Learning is a rapidly growing field that has the power to change the way many tasks are performed. This tutorial serves as a basic introduction to Machine Learning for those who are unfamiliar with the topic. To fully understand the neural networks that make up the main stream of Machine Learning, the neural functions in biology are first discussed, followed by the translation of these concepts to artificial neurons as an example of biomimicry. Furthermore, basic knowledge and skills are given to analyze different neural networks. Finally, applications of this technology to medical imaging are mentioned, along with important improvements on the generic networks, including ResNet, GAN, LTSM, and quadratic networks.

Citation Download Citation

Joshua A. Goldwag and Ge Wang "Training artificial neurons: an introduction to machine learning", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131P (10 September 2019); https://doi.org/10.1117/12.2530718

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