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Re: [Gneuralnetwork] mathematical background


From: Jean Michel Sellier
Subject: Re: [Gneuralnetwork] mathematical background
Date: Wed, 23 Mar 2016 19:04:55 +0100

I agree with the list of mathematical knowledge required that you reported in your previous email.

Personally, I would recommend the book of Bishop, "Neural Networks for Pattern Recognition".

I hope this helps somehow,

JM


2016-03-23 18:41 GMT+01:00 Ivan F. V. B. <address@hidden>:
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.




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