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Closed 10 years ago.
My question, his question, but blogs as resources to be specific.
I find blogs great to keep up to date... refresh material...
So do you know any blogs who tackle math-related programming problems...
Not exactly what you're asking for, but Project Euler freshens up my math skills.
Following MIT Open Courseware is another good computer-based way to learn and practice these skills.
Good Math, Bad Math is my favorite regular math blog.
Steve Yegge's post Math for Programmers gives a pretty decent rundown of what math is important for programmers to understand.
I also like to keep an eye on the math subreddit.
Better Explained has several good articles.
This blog has some interesting math-related things. Some of them are pretty high-level. You've been warned.
Not exactly a blog, but: Notices of the American Mathematical Society
I blog about programming and math, especially probability and statistics, at The Endeavour.
If you want highly lucid explanations and discussions of mathematics related to computer science, then this blog, Developing for Developers, is superb. [No posts for a while, but the previous posts are great.]
It sometimes goes off on tangents but this guy blogs about math software a lot
www.walkingrandomly.com
More along the lines of Project Euler than a blog, William Wu has quite a number of math and CS challenges.
MathPuzzle is one of my favorite sites on math. It may seem not directly related to problem-solving, but games are an excellent way of learning.
This Weeks Finds in mathematical physics is well worth a look, though whethers it's maths depends on where you draw the borderline.
good question, I'm surprised to see that nobody mentioned wikipedia so far.. However, many articles, especially about higher math are written by experts and are overloaded with details, which is not ideal if you just want to learn a bit. But still wikipedia is something that I use regularly to look up math questions.
When it comes to a specific math subject you may also want to read a book.. ;)
Found this one with the help of stackoverflow... :)
link
not before you have your first coffee I think...
Related
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Closed 11 years ago.
(edited)
For anyone interested in music and artificial intelligence:
Do you know of any music-composing algorithm that produces really interesting, fun or intelligent music? And not something sounding like a random noise.
(Previous, too broad question:)
What are some state of the art (very good, non-boring) music composition algorithms, software, researches that you have heard of?
Feel free to post any interesting link about this subject.
P.S. I don't mean programs that assist you at playing, but primarily anything that can compose melody by itself (or with little assistance).
OR: Analyses existing music pieces and tells how much it likes them :)
One of the leading researchers in algorithmic composition is David Cope of the University of California, Santa Cruz. His approach emphasizes machine learning, the results of which were impressively demonstrated in a 2006 performance.
http://www.wired.com/wired/archive/14.09/posts.html?pg=3
A good place to start would be with his aptly named book, The Algorithmic Composer, which covers much of his approach and provides most of the software he has written for his work.
http://books.google.com/books?id=rFGH07I2KTcC
Though not specifically algorithmic composition another invaluable resource is David Temperley's book, The Cognition of Basic Musical Structures, which provides quite a few models begging to be implemented.
http://books.google.com/books?id=IDoLEvTQuewC
Those two alone a pretty time consuming for anyone with an interest in that they are concrete enough that experimenting along the way is inevitable.
Hope that helps.
One possibility would be to use a hidden Markov model: feed it samples of music, and have it generate "similar" music.
One example: http://www.cogs.susx.ac.uk/users/christ/talks/music-making-with-HiMMs.pdf
I did something similar with Shakespeare's sonnets. The results were ... interesting. Amusing, at times.
There's a search engine that lets you whistle a tune and that searches for music alike. I'm not sure whether http://www.midomi.com/ is what I originally heard of. You can for example play the music and see if it finds what you intended.
A fellow student of me created a score composer for his Master's project. The input was humming or whistling and through FFT, music theory and combinatorial algorithms (I'm not sure whether it was simulated annealing). I'm not sure how it was related, but the project had something to do with the http://www.wikifonia.org/ project.
(edit)
I heard a talk from someone who worked at http://last.fm. They analyze music (machine learning) as one of the ways to overcome the cold start problem in their recommender system. They try to predict how much a new song resembles other songs.
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Closed 10 years ago.
I have some experience with python, I asked for a new language, and said that i am having a hard time implementing what I have learned. they suggested I learn SICP. Saying it uses a great language and teaches great programming fundamentals.
But I notice it was published in 1984. Do you guys recommend it, or have I been trolled? :p
Thanks.
Yes, SICP is still a great book! The second edition, which is available online, as of 1996. Although, if you just want to learn Scheme instead of fundamental computer science, you might be better off with Teach Yourself Scheme in Fixnum Days.
I strongly encourage you to check out the book How to Design Programs. It focuses on the fundamentals of programming, not on the specific language, but it also uses Scheme as its language. It's also available free online.
You can also check out the current release of the second edition, which is in preparation (or the less-stable but more up-to-date current draft).
Firstly, you're loooking at the first edition. The second edition is from 1996.
You should VERY MUCH tackle the book. I've gone through about half and my mind is blown. I can't begin to explain how amazing it is. Not only will you develop an appreciation for elegance in programming, but you'll see the line blurred between coding and computer science.
Don't approach this book like a programming book. Approach it as if you want to learn the fundamentals of computation and computer science using programming as a means of expression.
SICP is one of the best books I've read for learning how to write programs well. I never used scheme outside of the work I did in that book, but it's well worth your time.
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Closed 9 years ago.
So, those of us who've been answering questions and dealing with bug reports for a while are all deeply familiar with the concept of a "complete, minimal example". You start with a gnarly huge pile of code that's got a weird error, and you want to ask someone about it. But you don't want to send them the whole mess, so you start cutting the pile of code down into smaller pieces, until you get this little crystalized 20-line gem that still has the same bug you started with.
(And then, at least half the time, you realize what the bug is because it's staring you in the face like a luna moth, and you don't have to ask after all. But that's a side effect.)
Doing that sort of code-reduction isn't a skill that everyone has -- it takes a bit of practice, beyond the fact that a lot of newbies haven't yet learned that they ought to do it in the first place. There are several excellent sets of instructions for newbies on how to ask good questions (e.g., ESR's classic "How To Ask Questions The Smart Way"). Are there good instructions out there that explain what exactly a minimal complete example is, and how and why to create them?
It's all in the name. A complete minimal example is an example that is complete and minimal. Complete means that it contains all pertinent information, and minimal means it contains no information that is not pertinent. The ability to identify what is an is not relevant is something that only comes with practice - trying to provide instructions for this seems a little like trying to provide instructions on riding a bicycle. You can give the technical explanation and describe the mechanics (and the TeX FAQ you linked to does that very well) but in the end the only way to really learn is by doing.
I'll start this off with the ones I'm familiar with; these both come out of the TeX community, and so are a little oblique for programming. I'm hoping other people have better answers.
The TeX FAQ has an entry on How to make a “minimum example”.
That page points to a somewhat longer article on "What is a minimal working example?". Again, this is for TeX documents; the ideas are the same, but the tools and details a little different.
Edit (2013/01): Another one that I noticed in a StackOverflow comment: The "Short, Self Contained, Correct (Compilable), Example" page. Not perfect, and it suggests 20kb (!) as an upper limit, but a good addition to the list.
Edit (2013/02): Jon Skeet has a blog post about writing good questions, which includes a section on sample code that seems useful.
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Closed 10 years ago.
At some point in our lives we're put in the situation to learn a new language (either by job requirements or just passion). Personally, I'm trying to learn Objective-C coming from a background of several years coding php. My problem is that I'm bored with your average starting programs (most of them coming from maths; eg: Fibonacci).
What are your first algorithms and programs you write when learning a new language?
I'm thinking we could make a list of interesting problems to be solved and that would make the learning curve at least more entertaining.
Later edit:
My question is about fun things (and also useful), but not like Code Kata or other very boring math stuff (I've done many of those already)
Later edit [ 2 ]:
I found this today and the problems there seem fun to do.
Try to develop something proper, a project of some kind; solving algorithmic problems really only teaches you to apply the algorithm, not really a lot about the language your using.
Deciding on a useful project then deciding on a language and then 'doing it' will cover many of the bases you need to learn a new language.
I like this series of articles by Larry O'Brien.
http://www.knowing.net/index.php/2006/06/16/15-exercises-to-know-a-programming-language-part-1/
http://www.knowing.net/index.php/2006/06/16/15-exercises-to-know-a-programming-language-part-2-data-structures/
http://www.knowing.net/index.php/2006/06/16/15-exercises-to-know-a-programming-language-part-3-libraries-frameworks-and-mashups/
Have a look at these:
How do you find interesting problems to solve?
https://stackoverflow.com/questions/6327/what-are-your-programming-exercises
Just as an idea: Look at the problems of the The Computer Language Benchmarks Game.
When you've done a snippet, you can compare your solutution to the provided one.
Whenever I pick up a new language, I try using it to solve the problems at SPOJ. They basically support every mainstream language out there (as well as a few esolangs like Intercal and Whitespace). The problems are mostly from various ACM and similar format contests around the world and so can be quite hard, and perhaps be too CS/math for everyone's tastes. The tutorial problems are quite doable in any language.
I would also recommend adding that language's tag to your SO interested tags list. Many SO questions involve small snippets of code, and trying to answer them and looking at the other answers can be very enlightening.
But as soon as I've got the basics right, the strategy I choose is to try my hand at a very small project. Usually this a simple game, GUI app or a tiny webapp. This is valuable since there are some things you cannot learn by only doing small isolated blobs of code.
Try to solve these challenges using your new language. What's it all about:
In software we do our practicing on
the job, and that’s why we make
mistakes on the job. We need to find
ways of splitting the practice from
the profession. We need practice
sessions...
I sometimes use the exercises from the first few chapters of Structure and Interpretation of Computer Programs.
You can also try to understand or improve existing open source projects as suggested in this so question. Even though the answers are cocoa biased it should be a good starting point.
This way you can dive in to the level of your choice (understand, debug, improve) and in direction of your interest (UI, specific library, etc, etc...).
EDIT: When I said starting point, I meant that you can browse repositories for objective-c code, for example sourceforge
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Closed 10 years ago.
I have been trying to grasp the basics of Support Vector Machines, and downloaded and read many online articles. But still am not able to grasp it.
I would like to know, if there are some
nice tutorial
sample code which can be used for understanding
or something, that you can think of, and that will enable me to learn SVM Basics easily.
PS: I somehow managed to learn PCA (Principal Component Analysis).
BTW, you guys would have guessed that I am working on Machine Learning.
The standard recommendation for a tutorial in SVMs is A Tutorial on Support Vector Machines for Pattern Recognition by Christopher Burges. Another good place to learn about SVMs is the Machine Learning Course at Stanford (SVMs are covered in lectures 6-8). Both these are quite theoretical and heavy on the maths.
As for source code; SVMLight, libsvm and TinySVM are all open-source, but the code is not very easy to follow. I haven't looked at each of them very closely, but the source for TinySVM is probably the is easiest to understand. There is also a pseudo-code implementation of the SMO algorithm in this paper.
This is a very good beginner's tutorial on SVM:
SVM explained
I always thought StompChicken's recommended tutorial was a bit confusing in the way that they jump right into talking about bounds and VC statistics and trying to find the optimal machine and such. It's good if you already understand the basics, though.
Lots of video lectures on SVM:
http://videolectures.net/Top/Computer_Science/Machine_Learning/Kernel_Methods/Support_Vector_Machines/
I found the one by Colin Campbell to be very useful.
A practical guide to SVM classification for libsvm
PyML Tutorial for PyML
I think 1 is practical for use, 3 is clear for understanding.
Assuming you know the basics (eg max margin classifiers, constructing a kernel), solve Problem Set 2 (handout #5) of that stanford machine learning course. There's answer keys & he holds your hand through the whole process. Use Lecture notes 3 & video #7-8 as references.
If you don't know the basics, watch earlier videos.
I would grab a copy of R, install the e1071 package which nicely wraps libsvm, and try to get good results on your favorite data sets.
If you just figured out PCA, it might be informative to look at data with many more predictors than cases (e.g., microarray gene expression profiles, time series, spectra from analytical chemistry, etc.) and compare linear regression on the PCA'd predictors with SVM on the raw predictors.
There are a lot of great references in the other answers, but I think there's value in playing around with the black box before you read what's inside.