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Reddit mentions of Elements of ML Programming, ML97 Edition (2nd Edition)

Sentiment score: 1
Reddit mentions: 2

We found 2 Reddit mentions of Elements of ML Programming, ML97 Edition (2nd Edition). Here are the top ones.

Elements of ML Programming, ML97 Edition (2nd Edition)
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Found 2 comments on Elements of ML Programming, ML97 Edition (2nd Edition):

u/phao ยท 8 pointsr/cscareerquestions

The best way I know how is by solving problems yourself and looking at good solutions of others.

You could consider going back to "fundamentals".

Most programming courses, IMO, don't have nearly as many exercises I think they should have. Some books are particularly good on their exercises list, for example K&R2, SICP, and TC++PL. Deitel's has long exercises lists, but I don't think they're particularly challenging.

There are some algorithms/DS books which focus on the sort of problem solving which is about finding solutions to problems in context (not always a "realistic" one). Like the "Programming Challenges" book. In a book like that, a problem won't be presented in a simple abstract form, like "write an algorithm to sort numbers". It'll be inside some context, like a word problem. And to solve that "word problem", you'll have to find out which traditional CS problems you could solve/combine to get the solution. Sometimes, you'll just have to roll something on your own. Like a new algorithm for the problem at hand. In general, this helps you work out your reduction skills, for once. It also helps you spotting applications to those classical CS problems, like graph traversal, finding shortest plath, and so forth.

Most algorithms/DS books though will present problems in a pretty abstract context. Like Cormen's.

I think, however, people don't give enough credit to the potential of doing the exercises on the books I've mentioned in the beginning.

Some books I think are worth reading which also have good exercises:

u/moyix ยท 1 pointr/programming

Much in the same theme, I really like the cover to "Elements of ML Programming" by Jeffrey D. Ullman. Couldn't find a standalone image, but the "Look inside" portion of Amazon's page is legible.