HTML5 & CSS3 Fundamentals: Development for Absolute Beginners 10 minutes, 20 seconds 35 minutes, 14 seconds 29 minutes, 21 seconds 25 minutes, 25 seconds 48 minutes, 58 seconds 20 minutes, 18 seconds 6 minutes, 59 seconds 15 minutes, 30 seconds 13 minutes, 0 seconds 27 minutes, 27 seconds DS-GA 1003: Machine Learning and Computational Statistics, Spring 2015 This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve both the traditional and the novel data science problems found in practice. This course will also serve as a foundation on which more specialized courses and further independent study can build. This is a required course for the Center for Data Science's Masters degree in Data Science, and the course is designed for the students in this program. Other interested students who satisfy the prerequisites are welcome to take the class as well. Course details can be found in the syllabus. This term we will be using Piazza for class discussion. See the Course Calendar for all schedule information. For registration information, please contact Varsha Tiger.
NYU > Courant Institute > CIMS Bulletin Thursday, December 10, 2015 Analysis Seminar Title: Optimal Hardy-type inequality for nonnegative second-order elliptic operator: an answer to a problem of Shmuel Agmon Yehuda Pinchover, Technion Applied Mathematics Lab Seminar Title: Science-Driven Robots to Study the Fluid Mechanics of Animal Propulsion Michael Triantafyllou, MIT Friday, December 11, 2015 Numerical Analysis And Scientific Computing Seminar Title: Transport of probability measures in high dimensions with applications to Bayesian inference Alessio Spantini, MIT Probability Seminar Title: Asymptotics in periodic TASEP with step initial condition Zhipeng Liu, CIMS Title: A universality result for the random matrix hard edge Brian Rider, Temple University Computer Science Colloquium Title: GIVEN A NETWORK, PREDICT ITS FUTURE Roger Guimera, ICREA and Rovira i Virgili University Graduate Student And Postdoc Seminar Title: Odometers, cutting and stacking, graphs, and flat surfaces: a magic trick Rodrigo Treviño, Applied Mathematics Seminar
Interview Brute force Algorithm. Have 2 for loops for i = 1 to i less than array.length -1 for j=i+1 to j less than array.length This way you can get substring of every possible combination from the array Have a palindrome function which checks if a string is palindrome so for every substring (i,j) call this function, if it is a palindrome store it in a string variable If you find next palindrome substring and if it is greater than the current one, replace it with current one. Finally your string variable will have the answer Brute force approach for this problem takes O(n3) time. Another approach is Reverse the string and store it in different string Now find the largest matching substring between both the strings This too will take O(n2) time We can solve this problem using suffix trees, but constructing the suffix tree itself seems to be more complex in terms of both time and space complexity. For instance, position 2 in the string "racecar" would start as: Runtime: JavaCode:
c UNIX Tutorial - Introduction What is UNIX? UNIX is an operating system which was first developed in the 1960s, and has been under constant development ever since. By operating system, we mean the suite of programs which make the computer work. It is a stable, multi-user, multi-tasking system for servers, desktops and laptops. UNIX systems also have a graphical user interface (GUI) similar to Microsoft Windows which provides an easy to use environment. Types of UNIX There are many different versions of UNIX, although they share common similarities. Here in the School, we use Solaris on our servers and workstations, and Fedora Linux on the servers and desktop PCs. The UNIX operating system The UNIX operating system is made up of three parts; the kernel, the shell and the programs. The kernel The kernel of UNIX is the hub of the operating system: it allocates time and memory to programs and handles the filestore and communications in response to system calls. The shell Files and processes A file is a collection of data.
Description - Assignment 2 | Kaggle in Class My submission is a very simple architecture to give you some guidance Architecture: Conv layer (23 channels, 7x7 filters, stride 2, padding 2, RELU activation)Max pooling (3x3 patch, stride 2)DropoutFull connected layer (50 units)Softmax layer Input preprocessing:Per channel and per pixel mean subtraction Training:batch size 128learning rate 0.001learning rate annealed by 0.998 every epochTrained on random 4500 examples, used remainder for validation.
Networks, Crowds, and Markets: A Book by David Easley and Jon Kleinberg In recent years there has been a growing public fascination with the complex "connectedness" of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else. Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. The book is based on an inter-disciplinary course that we teach at Cornell. You can download a complete pre-publication draft of Networks, Crowds, and Markets here.
Raleigh: Technical Papers Wednesday, 2:00 – 3:30 PM Classification-Enhanced Ranking [PDF] Paul N. Bennett, Krysta Svore, Susan Dumais Ranking Specialization for Web Search: A Divide-and-Conquer Approach by Using Topical RankSVM Jiang Bian, Xin Li, Fan Li, Zhaohui Zheng, Hongyuan Zha Generalized Distances between Rankings Ravi Kumar, Sergei Vassilvitskii Predicting Positive and Negative Links in Online Social Networks [PDF] Jure Leskovec, Daniel Huttenlocher, Jon Kleinberg Empirical Comparison of Algorithms for Network Community Detection Jure Leskovec, Kevin Lang, Michael Mahoney [PDF] Modeling Relationship Strength in Online Social Network [PDF] Rongjing Xiang, Jennifer Neville, Monica Rogati Collaborative Location and Activity Recommendations with GPS History Data [PDF] Vincent W. Find Me If You Can: Improving Geographical Prediction with Social and Spatial Proximity [PDF] Lars Backstrom, Eric Sun, Cameron Marlow Equip Tourists with Knowledge Mined from Travelogues [PDF] Qiang Hao, Rui Cai, Changhu Wang, Lei Zhang
Hacker's guide to Neural Networks Note: this is now a very old tutorial that I’m leaving up, but I don’t believe should be referenced or used. Better materials include CS231n course lectures, slides, and notes, or the Deep Learning book. Hi there, I’m a CS PhD student at Stanford. I’ve worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS - a Javascript library for training Neural Networks. Javascript allows one to nicely visualize what’s going on and to play around with the various hyperparameter settings, but I still regularly hear from people who ask for a more thorough treatment of the topic. This article (which I plan to slowly expand out to lengths of a few book chapters) is my humble attempt. My personal experience with Neural Networks is that everything became much clearer when I started ignoring full-page, dense derivations of backpropagation equations and just started writing code. “…everything became much clearer when I started writing code.”