
Ask HN: Best book on machine learning? - hartator
I am wondering what the HN community will recommend as the best book on machine learning, specially for computer vision with Caffe or TensorFlow.
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curuinor
You're not asking about anything about computer vision, if you're asking just
about Caffe and Tensorflow. You're not even asking about the whole of machine
learning: you're just asking about neural networks.

If you haven't done significant mathematical stuff by yourself (like, do all
the problems in a math book by yourself without anyone else telling you what
to do), go get a degree. MS in CS is probably best, those are surprisingly
easy to get into.

Otherwise, Haykin's Neural Networks and Learning Machines is best in my
opinion ([http://www.amazon.com/Neural-Networks-Learning-
Machines-3rd/...](http://www.amazon.com/Neural-Networks-Learning-
Machines-3rd/dp/0131471392)). Bengio's Deep Learning book is the most current
by far ([http://www.deeplearningbook.org/](http://www.deeplearningbook.org/)).
Otherwise, you read papers.

Note that all of these will deal nearly exclusively with the mathematics.
Otherwise, you are following tutorials and wouldn't need any actual knowledge
to do things. Some of the mathematics is a year old: some of the mathematics
is 50 years old, some of it has been around since Gauss.

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e19293001
With a knowledge in college algebra, what math book would you study next?

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curuinor
If by "college algebra" you mean, abstract or modern algebra (groups, rings,
fields, modules, vector space, lattices, etc), I would throw away the modern
algebra book (for now: there are some applications of topology and topology-
related parts of algebra, but without a solid grounding in analysis and
numerical computing you won't get anywhere) and get some analysis books. (baby
(Principles of Mathematical Analysis) along with big (Real and Complex
Analysis) Rudin if you want to do theoretical things, or just normal calculus
books like Spivak's if you want to do things). Then, learn computational
linear algebra (I'm not appraised of too many great books in this, although
try looking at the ee263 notes for Stanford, for linear dynamical systems
([http://web.stanford.edu/class/archive/ee/ee263/ee263.1082/no...](http://web.stanford.edu/class/archive/ee/ee263/ee263.1082/notes/ee263coursereader.pdf\)))

If by "college algebra" you mean that you learned basic algebra (fundamental
theorem, equation solving, memorize solution to quadratic equation) in
college, you need to learn calculus in one and then many dimensions (most of
the people I know who are doing research-level things in machine learning
learned this stuff in 6th to 8th grade, but who cares, especially if you are
merely doing applications). Then, you learn linear algebra.

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brudgers
Curious about your current level of expertise in those areas.

