Blocks - closures and anonymous functions to c and derivative languages

I get a lot of inbound requests for book recommendations from people who want to either get into research, or better understand it so they can build high quality supporting systems for research and analytics.  The typical desire is participate in some form of what statisticians call inference, or what machine learning people call learning (the two are more or less the same).

anic is the reference implementation compiler for the experimental, high-performance, statically-safe, fully implicitly parallel, object-oriented, general-purpose dataflow programming language ANI.

Portably written using the GNU toolchain, anic works on all of the popular operating systems, including *nix, Mac OS X, and Windows (via Cygwin).

Gremlin is a Turing-complete, graph-based programming language developed in Java 1.6+ for key/value-pair multi-relational graphs known as property graphs. Gremlin makes extensive use of the XPath 1.0 language to support complex graph traversals. This language has applications in the areas of graph query, analysis, and manipulation.

As you all may know, I watched and posted my lecture notes of the whole MIT Introduction to Algorithms course. In this post I want to summarize all the topics that were covered in the lectures and point out some of the most interesting things in them.

Actually, before I wrote this article, I had started writing an article called “The coolest things that I learned from MIT’s Introduction to Algorithms” but quickly did I realize that what I was doing was listing the topics in each article and not really pointing out the coolest things. Therefore I decided to write a summary article first (I had promised to do so), and only then write an article on really the most exciting topics.

Talking about the summary, I watched a total of 23 lectures and it resulted in 14 blog posts. It took nearly a year to publish them here. The first blog post in this series was written in August 2008, and the last in July 2009.

Computer Science is facing a major roadblock to further research. The problem is most evident with students, but afflicts many researchers as well: people simply have a tough time inventing research topics that sound sufficiently profound and exciting. Many PhD students waste needless years simply coming up with a thesis topic. And researchers often resort to reading documents from government grant agencies so they will know what to work on for the next proposal!

Good news for the CS community: the problem has at last been solved. The table below provides the answer.

The book aims to provide a modern approach to information retrieval from a computer science perspective. It is based on a course we have been teaching in various forms at Stanford University and at the University of Stuttgart.

These recommendations are sorted from general to specific: from resources for any kind of graduate student to resources for graduate students in my own field, static analysis. My criteria for inclusion are readability, self-containment, brevity and coverage. If you're a grad student at Utah, feel free to come by my office and borrow any of them for a day or two. (I've provided links for the books and papers I could find online; for the rest, I've provided amazon.com links to the most recent edition.)

Came for the splashpage, stayed for the good times.

We currently maintain 176 data sets as a service to the machine learning community. You may view all data sets through our searchable interface. Our old web site is still available, for those who prefer the old format. For a general overview of the Repository, please visit our About page. For information about citing data sets in publications, please read our citation policy. If you wish to donate a data set, please consult our donation policy. For any other questions, feel free to contact the Repository librarians. We have also set up a mirror site for the Repository.

Describes how Scheme optimizes things (i.e.: why you want to use tail-recursion).

a packet manipulation framework for python

linked via http://www.twine.com/item/1155l7g42-66s/parallel-and-cluster-computing-with-mpi4py

Even slime mould can work out the shortest route through a maze, scientists have found. They say the discovery shows that the most primitive amoeba-like organisms have some basic computing ability.

This paper presents a tutorial on those aspects of floating-point that have a direct impact on designers of computer systems. It begins with background on floating-point representation and rounding error, continues with a discussion of the IEEE floating-p

some nifty lecture notes about data structures. they're very concise.

Alex Smith, a undergraduate electrical engineering student at the University of Birmingham in the United Kingdom, has proven that a primitive type of computer known as a 2,3 Turing machine can solve every computational problem there is. Proving the "unive

This page provides pointers to commonly used data analysis and visualization algorithms. An 'IVC Software Framework' was implemented to facilitate the easy integration of diverse software packages and their menu driven usage. Many software packages are av

This book will help you to learn the Python programming language, whether you are new to computers or an experienced programmer. let's begin!

SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python.

Another wicked R tutorial.

'(schemers . org): an improper list of Scheme resources

Kent Dybvig's guide to debugging Scheme.

A huge repository of scheme code.

The page for my AI textbook.

A guide to using Java for real-time system.

*THE* Quine page.

16 chapter/91 essay book written by the guys who brought you Basecamp, Campfire, and Backpack.

A recreation of a difference engine, the first computer, in the medium of LEGO.

A revolution is changing the design and teaching of introductory computer science curricula. The TeachScheme! Project is a leading innovator in these new curricula at the high school and college levels. The curriculum is in use at hundreds of high schools

Free css menus.

Computational complexity theory From Wikipedia, the free encyclopedia. Jump to: navigation, search In computer science, computational complexity theory is the branch of the theory of computation that studies the resources required during computation to so

A network of snail brain cells has been cultivated on a silicon chip, an advance that its creators claim is a big step towards microchip implants that can control artificial limbs, restore sight and revive memory function. Researchers from Calgary Univers

Scheme code and tutorial for playing with integers, including a buggy prime number checker.

I/O in Chez Scheme

Polymorphism in Scheme