tag:blogger.com,1999:blog-22587889.post7112394182068938457..comments2019-02-26T11:57:57.700+05:30Comments on Ruminations of a Programmer: Randomization and Probabilistic Techniques to scale up Machine LearningUnknownnoreply@blogger.comBlogger3125tag:blogger.com,1999:blog-22587889.post-18572454227683225092016-07-28T13:04:38.977+05:302016-07-28T13:04:38.977+05:30Thanks for your post. I found it extremely useful ...Thanks for your post. I found it extremely useful as I work as a programmer, too. I'm highly interested in stream computing to. Every day I deal with huge amount of data and it's good to know how to deal with it and how to use randomization and probabilistic techniques to scale up Machine Learning.microsoft dynamic axhttps://ax-dynamics.com/microsoft-dynamics-ax/noreply@blogger.comtag:blogger.com,1999:blog-22587889.post-59071161668655332712015-04-25T16:29:50.304+05:302015-04-25T16:29:50.304+05:30> Singular Value Decomposition is a dimensional... > Singular Value Decomposition is a dimensionality reduction technique to unearth a smaller number of intrinsic concepts from a high dimensional matrix by removing unnecessary information. It does so by projecting the original matrix on to lower dimensions such that the reconstruction error is minimized<br /><br />No, that's Principal Component Analysis (PCA). SVD is just a special kind of matrix decomposition, which may be used to do PCA (other algorithms exist as well).Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-22587889.post-47197454860289307092015-04-20T12:54:42.213+05:302015-04-20T12:54:42.213+05:30Thank you for this article. It is very informative...Thank you for this article. It is very informative. I do hope that you will continue to share your experiences in this field :) \.Koohyarnoreply@blogger.com