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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, which I copied here for convenience from ?

- 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

A more extensive syllabus on math background can also be found in the
Introduction to Machine Learning - Cambridge University Press 2008
available online at
but not under a free (libre) license.

Do you know any other good resource on math background?

Ivan F. V. B.

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