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I've done that course, and some these files look very similar to the boilerplate code that you have to modify/complete for the weekly coursework. Comments seem

I've done that course, and some these files look very similar to the boilerplate code that you have to modify/complete for the weekly coursework. Comments seem to have been removed and some refomatting done. Edit: As jszymborski points out, there an attribution of the source in the readme.md.

jszymborski 8y ago
Some attribution seems to be there in the beginning of the README > "In most cases the explanations are based on [this great](https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning) machine learning course." EDIT: It was added 6hrs ago https://github.com/trekhleb/machine-learning-octave/commit/f87d3d0afcecf546e3a5282a70890615233b8536#diff-04c6e90faac2675aa89e2176d2eec7d8 https://github.com/trekhleb/machine-learning-octave/commit/f...
- trekhleb 8y ago
  Yes, links to the Andrew Ng course is on every README of the repo: on top of the main README.md and in the "References" section of each internal README.md.
trekhleb 8y ago
Yes the code and comments were reformatted for better reading (in my opinion). Code has also been split differently than in the course: I was trying to distinguish some similarities between the algorithms like separate hypotheis() function, separate gradient_descent_step() function and so on. The most significant changes have been made to the neural-network section since it now supports multiple internal layers of different size for the neural network. The course's version of the code supported only one internal layer. Also README file contains additional images (sometimes animations) and definitions for additional learning experience.