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PUBLIC MARKS with tag optimization

November 2008

Image Optimization, Part 3: Four Steps to File Size Reduction » Yahoo! User Interface Blog

by Xavier Lacot & 2 others
This post is about some common tools you can use to reduce the file size of your images. The idea is to be able to just take the images your designer has created and instead of using them “as is”, go ahead and tidy them up in short time and no effort, without even looking at them.

October 2008

Éviter les événements trop fréquents — Performance web

by claire_ & 1 other
Les bons développeurs javascript utilisent les événements, à toutes les sauces. En fait quasiment tout ce qui est fait en javascript est en réaction à un événement. On arrive à deux problématiques qui ont un un impact plus ou moins important sur les performances :

smush it!

by vrossign & 7 others
* You can upload a bunch of pictures in your browser * You can provide us with a list of image urls or * You can get a Firefox Extension to optimize the images found on any web page Saving bytes has never been so easy - you point us in the right direction, and we'll do the rest for you. A

smush it!

by Xavier Lacot & 7 others
SmushIt is an online tool that optimizes images. A firefox extension is available, that allows to optimize all the images of one web page at one stretch.

September 2008

August 2008

PageTest Web Page Optimization and Performance Test

by loneseb
PageTest allows you to provide the URL of a webpage to be tested. The test will be conducted from the location specified and you will be provided a waterfall of your page load performance as well as a comparison against an optimization checklist.

July 2008

June 2008

DeepLearningWorkshopNIPS2007 < Public < TWiki

by ogrisel (via)
Theoretical results strongly suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need "deep architectures", which are composed of multiple levels of non-linear operations (such as in neural nets with many hidden layers). Searching the parameter space of deep architectures is a difficult optimization task, but learning algorithms (e.g. Deep Belief Networks) have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This workshop is intended to bring together researchers interested in the question of deep learning in order to review the current algorithms' principles and successes, but also to identify the challenges, and to formulate promising directions of investigation. Besides the algorithms themselves, there are many fundamental questions that need to be addressed: What would be a good formalization of deep learning? What new ideas could be exploited to make further inroads to that difficult optimization problem? What makes a good high-level representation or abstraction? What type of problem is deep learning appropriate for? The workshop presentation page show selected links to relevant papers (PDF) on the topic.

6 FREE Tools To Get The Best Keywords For Your Site | MakeUseOf.com

by simon_bricolo
tools which can help you research different keywords specific to your site and optimize the site.

May 2008

CVXMOD – Convex optimization software in Python

by ogrisel
CVXMOD is a Python-based tool for expressing and solving convex optimization problems. It uses CVXOPT as its solver. It is developed by Jacob Mattingley, as PhD work under Stephen Boyd at Stanford University. CVXMOD is primarily a modeling layer for CVXOPT. While it is possible to use CVXOPT directly, CVXMOD makes it faster and easier to build and solve problems. Advanced users who want to see or manipulate how their problems are being solved should consider using CVXOPT directly. Additional features are being added to CVXMOD beyond just modeling. These are currently experimental. CVXMOD has a similar design philosophy to CVX, a convex optimization modeling language for Matlab®, and uses the principles of disciplined convex programming, as developed by Michael Grant, Stephen Boyd and Yinyu Ye.

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