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## [Gneuralnetwork] mathematical background

**From**: |
Ivan F. V. B. |

**Subject**: |
[Gneuralnetwork] mathematical background |

**Date**: |
Wed, 23 Mar 2016 18:41:32 +0100 |

**User-agent**: |
Mutt/1.5.21 (2010-09-15) |

Dear GNeuralNetworkers,
it is very exiting to have heard of this community and start to be part
of it.
Unfortunately, I must admit that my mathematical background is limited and
rusted. Thus my question:
Which mathematical fields would you recommend to revise/learn and to
which level of deepness?
Would you agree or extend the syllabus of the Machine Learning Nanodegree of
udacity.com, which I copied here for convenience from
https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009 ?
- Intermediate statistical knowledge
- Populations, samples
- Mean, median, mode
- Standard error
- Variation, standard deviations
- Normal distribution
- Precision and accuracy
- Intermediate calculus and linear algebra
- Derivatives
- Integrals
- Series expansions
- Matrix operations through eigenvectors and eigenvalues
Would anyone by interested in co-writing free (libre) accompanying materials
for understanding and using machine learning algorithms with gneuralnetwork,
including cute examples?
Little cute projects like this have some traction
https://github.com/yenchenlin1994/DeepLearningFlappyBird
A more extensive syllabus on math background can also be found in the
Introduction to Machine Learning - Cambridge University Press 2008
available online at http://alex.smola.org/drafts/thebook.pdf
but not under a free (libre) license.
Do you know any other good resource on math background?
Ivan F. V. B.

**[Gneuralnetwork] mathematical background**,
*Ivan F. V. B.* **<=**