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I want to improve my programming skills, when I participate in some programming competition I feel that every challenge is so tough that i can not solve it,I have good knowledge of coding but I fell to decide the algorithm needed to solve particular problem for that can anyone tell me which books to read
I would suggest first to get comfortable with programming language of your choice. Once you have confidence on your language and Data structure, you can proceed confidently for any programming challenges. Make a habit of writing complete code with all edge cases handled on a sheet of paper rather than simple pseudo code for your practice session.
Now to solve algorithmic problem first to grasp elementary algo functioning via book or online resources. If you are using coreman (good book for algo) then you might want to understand basic concepts of different sorting techniques, heap, queue, hashing, greedy and dynamic algorithm. For some topic i would recommend to research online as well - like dynamic programming and hashing. Almost 70-80% interview questions are either hashing or DP based. Then look for major examples and their solution for these algorithm. Once your mind will set up you would be able to think quickly for any algorithmic problem.
Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest , Clifford Stein is a good one to start with.
Covers almost everything, from graph theory to geometric algorithms and all related data structures, furthermore they use the commonly used "Big O" notation to indicate the efficiency of the algorithms explained. Most of the time multiple algorithms are presented for the same problem, together with their advantages and disadvantages.
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I am a novice to algorithms and data structures. I recently started participating on codeforces and spoj etc.for solving questions i need to study dp,greedy algorithms,graph algorithms,data structures.what should be my strategy for studying or rather sequence and what data structures i need to know for competitive programming?
All in all there shouldnt be an "optimal" sequence. Its all about understanding the topic. Since not two people can learn with the same speed, there shouldnt be something like an "optimal" sequence. But its good to learn basic approaches of each topic.
There are alot Tutorials out there, which explains the most fundamental thing in any topic. E.g. Youtube covers most graph problems. Even DP and so can be found there. Especially on Topcoder Tutorials there is alot you can learn.
On the other hand you will learn nearly nothing, if u dont have to think for yourself. So solving such puzzles is a must. I would recommend this site (especially for dp). Just check the "problem set" link on the site and look for dynamic programming.
I recommend you this book: Competitive Progamming, by Halim. Is very complete, and newbie-compatible.
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I'm currently working on a recommendation system that uses collaborative filtering. And now I'm researching for a good/efficient algorithm that is geared towards movie recommendation. I'm confused because there are many algorithm like the Pearson Correlation Coefficient. And so I don't know what to use/implement.
Can you give me a good/efficient algorithm? or a site that gives a good example or simulation to the algorithm?
Thanks for the help!
Give this paper about the netflix prize a read Netflix Prize. Usually the 'state of the art' is some variant of matrix factorisation such as OrdRec. Check out the Funk Blog FunkSVD which will give you a nice simple explanation about implementing the starts of a matrix factorization technique for CF.
Matrix factorisation (An example in Python) is a good starting point. Furthermore, I'd recommend Ed Chen's Blog and The Mining of Massive Datasets as good introductions to the variety of methods used to solve this type of problem. The interesting thing for me having worked with this type of data is the amount of sparseness, there are of course practical limits, papers by Emmanual Candes seem to shed light on this area, excellent advanced work.
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There are many Online Judges (OJ) for ACM/ICPC questions. And another Online Judge for Interview questions, named Leetcode (http://leetcode.com).
I think these OJs are very useful for us to learn algorithms. Recently, I am going to learn data mining algorithms. Is there any OJ for data mining questions?
Thank you very much.
There is MLcomp, where you can submit an algorithm and it will run it on a number of data sets to judge how well it is doing.
Plus, there is Kaggle, which hosts various classification competitions.
And of course you can do classes at Cousera. These are pretty much low level, but in order to get submission points you need to reproduce the known performance.
In particular the first also allows you to run several standard algorithms such as naive bayes and SVM and see how well they did. Obviously, your own implementation should perform similar then.
Unfortunately, both are pretty much focused on machine learning (i.e. classification and regression). There is very little in the unsupervised domain, clustering and outlier detection. On unlabeled data, things get too hard even to evaluate locally, so doing any kind of online judging is pretty much unsolved. What you can do is largely a one-class classification, or you just strip labels before running the algorithm.
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I have found numerous data-structures on wikipedia, also have also looked into several books in data-structures and found that they vary. I want to know what are the basic or minimum list of data-structure knowledge a new CS graduate should have?
Also is it necessary to know their implementation in more than one programming knowledge considering there is a difference in the implementation. If i know the implementation of Linked list in C should i know its Java based implementation?
It would be great if you could help me understand categorically:
Basic datastructures(necessary for a CS grad)
Advanced Datastructures
Edit : i am more interested in the list of data structures.
Have a look at Introduction to Algorithms by Cormen et al. In my experience, if you know what is in there you are set for anything coming at you.
I would not consider knowing any implementation very useful. If you know the basics you should be able to implement your own version quickly, but chances are you will never have to because there are libraries for that. So the rule for practice is: know your libraries!
Even so, it is important that you know properties of data structures (e.g. space overhead, runtimes of central operations, behaviour under concurrent accesses, (im)mutability, ...) so you will always use the one best suited to your task at hand.
This question is really a little too broad, even the way you've narrowed it down, because it depends on what sort of future path you're looking at. Grad school? PhD track? Industry? Which industry?
But as a rough minimum, I'd say, take a look at CLRS (as Raphael suggests) and pick out the following:
Linked lists, and the variations like stacks, queues, etc.
Basic heaps
Basic hash tables
Trees, especially including binary search trees, and preferably familiarity with at least one self-balancing BST
Graphs, both matrix-representation and adjacency list representation
And probably some more based on what sort of job you're looking for. As someone on a PhD track... well. All of them. At some point you will take a qualifier and be expected to know most of them.
Check out the MIT's OCW Intro to Algorithm Course It is great tutorial theoretically.
For practicing data structures in Java check : Data Structures & Algorithms in Java by Robert Lafore, it is excellent.
Implementation in one language is sufficient, but try to solve it in structured-oriented language like C and OO language like Java/ C++. This will help a lot while preparing for interviews.
One good resource for basic data structures in C : here
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I've been asked to recommend a resource (on-line, book or tutorial) to learn Algorithms (in the sense of of the MIT Intro to Algorithms) for non-CS or Math majors. Obviously the MIT book is way too involved and some of the lighter treatments (like OReilly's Algorithms in a Nutshell) still seem as if you would need to have some background in algorithmic analysis. Is there resource that presents the material in a way that developers who do not have a background in theoretical computer science will find useful?
I think the best way to learn algorithms are through the various competition sites.
USACO - my personal favorite, as it gives a clear path through the material
TopCoder - already mentioned
Sphere Online Judge - great if you want to work in another language other than C/C++/Java
As far as books, the best single intro I've seen for the non-math specialist is Data Structures and Algorithms. It takes you through an algorithm line by line and shows you how it decomposes mathematically, something CLRS's otherwise excellent analysis section is a little less clear on.
Skiena's Algorithm Design Manual is also excellent, as is his Programming Challenges, which is essentially a tutorial through the Valladolid Online Judge.
Honestly, though, I think the single most helpful thing a beginner can do is to implement the various algorithms -- merge sort, say, followed by Quicksort -- and time them against variously sized inputs. Create a spreadsheet with a graph that shows their growth over time. Very few non-specialists will have the patience or the know-how to set up a recurrence relation and solve their way through it. But you must understand the effect of, say O n^2 growth over time, and there's no better way to learn this than to watch your own program blow through its memory stack. :)
I say this as a non-CS, non-math programmer who has spent a good couple of months wrapping my mind around algorithmic analysis.
I'd go for the Algorithm Design Manual, by Steven Skiena. It's very readable and starts with the basics in an easy-to-understand way. For example, it explains big-O notation very well. The emphasis is on practical application, which is a big bonus for beginners coming from a non-theoretical field.
The second half of the book is a reference of common algorithm problems and practical approaches to their solutions. I found it invaluable as a learning aid, and now as a reference.
I'm not sure which MIT book you're referring to, but the canonical text is CLRS. I don't think it really assumes any background besides high school math.
Personally, I found doing TopCoder algorithm competitions over the course of the past few years to be the best way for me to learn common algorithms and put them into practice. Perhaps you should try the same. Whatever you do, I suggest that you spend a lot more hands-on-keyboard time implementing things you learn than head-in-book time, because that's the way to really internalize different techniques.