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Re: [Gneuralnetwork] mathematical background
From: |
Ivan F. V. B. |
Subject: |
Re: [Gneuralnetwork] mathematical background |
Date: |
Wed, 23 Mar 2016 19:58:57 +0100 |
User-agent: |
Mutt/1.5.21 (2010-09-15) |
* Jean Michel Sellier <address@hidden> [2016-03-23 19:05]:
> I agree with the list of mathematical knowledge required that you
> reported
>
> Personally, I would recommend the book of Bishop, "Neural Networks for
> Pattern Recognition".
Thanks Jean for your reply.
I remember that title as the bible in some circles, but that was like 20
years ago. Haven't a lot happend in that time, like deep learning
accomplishments, self driving cars and robotics?
Ivan F. V. B.
> 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?