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Why not: from Common Lisp to Julia

This article is a response to mfiano’s From Common Lisp to Julia which might also convey some developments happening in Common Lisp. I do not intend to suggest that someone coming from a Matlab, R, or Python background should pickup Common Lisp. Julia is a reasonably good language when compared to what it intends to replace. You should pickup Common Lisp only if you are interested in programming in general, not limited to scientific computing, and envision yourself writing code for the rest of your life. It will expand your mind to what is possible, and that goes beyond the macro system. Along the same lines though, you should also pickup C, Haskell, Forth, and perhaps a few other languages that have some noteworthy things to teach, and that I too have been to lazy to learn.

/I also do not intend to offend anyone. I’m okay with criticizing Common Lisp (I myself have done it below!), but I want t

@debasishg
debasishg / gist:8172796
Last active May 10, 2024 13:37
A collection of links for streaming algorithms and data structures

General Background and Overview

  1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
  2. Models and Issues in Data Stream Systems
  3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
  4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
  5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t