Description
PyData London 2016
In my pervious life, I was an astronomer and one of the big tasks many PhD students face is manual star classification. But why ask a student to do what a machine should be able to achieve quicker? In this talk, I use the spectra (stellar flux vs wavelength) of identified stars to build a classifier which will detect the identity of the stars I am interested in.
In my pervious life, I was an astronomer and one of the big tasks many PhD students face is manual star classification. But why ask a student to do what a machine should be able to do too? In this talk, I use the spectra (stellar flux vs wavelength) of identified stars to build a classifier which will detect the identity of the stars. Using a very simple non linear SVM, I achieve an 86% accuracy with my model. The next step is to use deep neural nets to achieve a better accuracy.
Slides available here: https://drive.google.com/file/d/0B2vz1haz5096b1ZEbkRKM3BaX3c/view?usp=sharing