This is material for a 1-day tutorial introducing some basic ideas in handling scientific data and applying machine learning methods to microscopy data. The idea is to work through the following files (available as Jupyter notebooks and html):
- coding.ipynb - introduction to Python approaches to data and image handling
- ml.ipynb - introduction to machine learning in the context of image analysis and Scanning Probe Microscopy (SPM), with main emphasis on Neural Networks
Each contains an opening lecture to outline some key ideas, followed by a series of tutorials that reinforce the concepts with practical applications.
There are also a selection of relevant articles in the literature folder that directly discuss the applications of machine learning in SPM.
- Background - we assume a basic knowledge of physical concepts and scanning probe microscopy. Understanding the basics of coding in any language will help, but is not essential.
- Software - the following packages should be installed:
- Anaconda (then all the following should be easily installable using conda)
- jupyter notebook (or jupyterlab)
- OpenCV
- scikit learn
- Tensorflow
- pytorch