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This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. If you find this work useful...

This is a diary that allows you to digitally write entries and view them. What makes this a masterpiece from its counterparts is how user data is created and abstracted. User data is protected and stored in PNG images, the image it self is not encrypted but the data inside the image is encrypted. If you open any one of the created PNG images, they look like blank images!. These "normal" looking images protect your information from prying eyes. The simplicity and elegance of my program stores your data(password and entry) locally and hidden from everyone.

This Matlab package implements machine learning algorithms described in the great textbook: Pattern Recognition and Machine Learning by C. Bishop (PRML). It is written purely in Matlab language. It is self-contained. There is no external dependency. This package requires Matlab R2016b or latter, since it utilizes a new Matlab syntax called Implicit expansion (a.k.a. broadcasting). It also requires Statistics Toolbox (for some simple random number generator) and Image Processing Toolbox (for reading image data). The code is extremely compact. ...

MatLab App for calculating fitting isotopic exchange profile data. Developed for measuring self-diffusivity and effective surface exchange coefficient of ion conducting materials. Requires MatLab image processing toolbox If you use this tool for you research, please cite my PhD thesis: Cooper, S. J. Quantifying the Transport Properties of Solid Oxide Fuel Cell Electrodes. (Imperial College London, 2015). @phdthesis{SJCooper2015, author = {Cooper, S.