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I am aware of the theory of stochastic gradient descent, which is a faster way of developing linear regression. Through this we can have an 'optimized implementation' of linear regression. There are similar techniques for non-parametric methods as well, which allows you to converge faster keeping in mind cost function.

I need suggestion for a book which has worked out implementations or examples of these type of optimized models with R/Python code or pseudo code. So that i can run sophisticated machine learning algorithms faster, without increasing my hardware further. I am open about increasing hardware though. What interests me is a faster implementation of techniques, so that i can use scalable implementations of machine learning algorithms for bigger data.

Thanks!!

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I cannot recommend a book, but if I need to look at some implementation of an advanced gradient descent method, I just search on GitHub. I believe a lot of new publications actually release the sources that go with the textbook on GitHub anyway. If you don't know the names of the methods, you can just look at the options provided by popular frameworks/libraries - i.e. descriptions and files

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  • $\begingroup$ I want to be able to create these optimized algorithms myself without using external packages, because if i want to convert my thought into technique i will not have to depend on a package developed by anyone else. In that regard, which is why i am looking for a book. Thanks! $\endgroup$ – Enthusiast Sep 8 '16 at 7:19
  • $\begingroup$ I'm not suggesting to use the package. Just read their code to understand how the specific advanced method differs from the one(s) you already know. Then implement what you have learned in your own code. $\endgroup$ – Iliyan Bobev Sep 8 '16 at 7:34
  • $\begingroup$ Yes, its another way to start though through debugging code and by trial and error. A book will however provide this in a structured and systematic manner. Which is why i am looking for a book. Thanks for your help though!! $\endgroup$ – Enthusiast Sep 8 '16 at 8:23

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