I really liked Scribd Analytics feature (and that we have racked up 3200 reads in 11 months for Poets and Hackers). I think Google Docs /Drive should really incorporate more Scribd like document sharing features including social (now that Slideshare is off the market for a relatively cheap $119m) and turn on analytics by default
I really liked the heatmap on the document feature in the second screenshot.
Anyways nice to see someone out there cares for Poets &….
Also a great slideshare in Japanese (no! Google Translate didnt work on pdf’s and slideshares and scribds (why!!) but still very lucid on using OAuth with R for Twitter.
Why use OAuth- you get 350 calls per hour for authenticated sessions than 150 calls .
I tried but failed using registerTwitterOAuth
There is a real need for a single page where you can go and see which social netowork /website is using what kind of oAuth, which url within that website has your API keys, and the accompanying R Code for the same. Google Plus,LinkedIn, Twitter, Facebook all can be scraped better by OAuth. Something like this-
In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract “topics” that occur in a collection of documents. An early topic model was probabilistic latent semantic indexing (PLSI), created by Thomas Hofmann in 1999. Latent Dirichlet allocation (LDA), perhaps the most common topic model currently in use, is a generalization of PLSI developed by David Blei, Andrew Ng, and Michael Jordan in 2002, allowing documents to have a mixture of topics. Other topic models are generally extensions on LDA, such as Pachinko allocation, which improves on LDA by modeling correlations between topics in addition to the word correlations which constitute topics. Although topic models were first described and implemented in the context of natural language processing, they have applications in other fields such as bioinformatics.
In statistics, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word’s creation is attributable to one of the document’s topics. LDA is an example of a topic model
Thats the world’s most widely read marketing textbook in slideshare format slides. You think you are a marketing guru expert at selling or promoting software- well spend 10 minutes flipping for a fun reading
and a presentation trying to be the worlds best presentation by putting social causes, geeky languages, hot looks in the same slides – Hi It is BO (not Barack Obama)
and if you are like me and suck at presentations , but unlike me would like to get better at presentations
if you are still reading this you probably have too much time on a Friday, so here is one YouTube poetry video I created while in a graphics design course in Vol State- it’s a mashuo of 12 poems, some Prezi, some music by that big proft making Google machine called You Tub
Carole Ann Matignon deals with optimization and scheduling, rules in the…….NFL!
Carole, We are waiting for the sequel on analytics on football and the beer game.
Social Media Screw-Ups
Social Media doesnt matter at all- Social Media matters a lot- Still undecided? Take a look
Scribd also is a great way to share content- and probably is small enough for. WordPress.com to allow embedding
Thats the reason why I sometimes prefer Scribd for sharing my poetry to Slideshare and Google Docs. Also I like the enhanced analytics and the much easier and evolved interface for reading. Slideshare is much more successful than Scribd because it is open to sharing with everyone- scribd tries to get you to register …;)
DEAP is intended to be an easy to use distributed evolutionary algorithm library in the Python language. Its two main components are modular and can be used separately. The first module is a Distributed Task Manager (DTM), which is intended to run on cluster of computers. The second part is the Evolutionary Algorithms in Python (EAP) framework.
The most basic features of EAP requires Python2.5 (we simply do not offer support for 2.4). In order to use multiprocessing you will need Python2.6 and to be able to combine the toolbox and the multiprocessing module Python2.7 is needed for its support to pickle partial functions.
EAP is part of the DEAP project, that also includes some facilities for the automatic distribution and parallelization of tasks over a cluster of computers. The D part of DEAP, called DTM, is under intense development and currently available as an alpha version. DTM currently provides two and a half ways to distribute workload on a cluster or LAN of workstations, based on MPI and TCP communication managers.
This public release (version 0.6) is more complete and simpler than ever. It includes Genetic Algorithms using any imaginable representation, Genetic Programming with strongly and loosely typed trees in addition to automatically defined functions, Evolution Strategies (including Covariance Matrix Adaptation), multiobjective optimization techniques (NSGA-II and SPEA2), easy parallelization of algorithms and much more like milestones, genealogy, etc.
We are impatient to hear your feedback and comments on that system at .
François-Michel De Rainville
Laboratoire de vision et systèmes numériques
Département de génie électrique et génie informatique
Quebec City (Quebec), Canada
and if you are new to Python -sigh here are some statistical things (read ad-van-cED analytics using Python) by a slideshare from Visual numerics (pre Rogue Wave acquisition)