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A comment on Mistakes Programmers Make when Starting in Machine Learning. Parent comment
Matlab/Octave is a great way to practice this due to the native data types. If python is your thing numpy's arrays are also pretty easy to digest.
Subtle little tricks like this: https://www.youtube.com/watch?v=evF-3ykjRU0
And understanding the dynamics of scalar operations vs matrix - vector operations.
The machine learning class has some good fundamentals if you need a refresher on how something works.
There will be more complex things like some optimization algorithms have different uses for eigen values: http://see.stanford.edu/materials/lsocoee364b/11-conj_grad_s...
See: http://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors
One other thing might be understanding different ways you can manipulate data. In this case, numerical representation here is an example per row when I toss in one matrix for training. This is applicable to many machine learning problems.
Matlab/Octave is a great way to practice this due to the native data types. If python is your thing numpy's arrays are also pretty easy to digest.
Subtle little tricks like this: https://www.youtube.com/watch?v=evF-3ykjRU0
And understanding the dynamics of scalar operations vs matrix - vector operations.
The machine learning class has some good fundamentals if you need a refresher on how something works.
There will be more complex things like some optimization algorithms have different uses for eigen values: http://see.stanford.edu/materials/lsocoee364b/11-conj_grad_s...
See: http://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors
One other thing might be understanding different ways you can manipulate data. In this case, numerical representation here is an example per row when I toss in one matrix for training. This is applicable to many machine learning problems.