firstmonday.org
Running code as part of an open standards policy | Shah | First Monday
http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2414/2201
How to cite item. Email this article (Login required). Email the author (Login required). Volume 14, Number 6 - 1 June 2009. Running code as part of an open standards policy. Background on open standards. Massachusetts open formats policy. Analyzing the failure of the open formats policy. When standards are open and freely available, it becomes possible for anyone to develop an interoperable implementation. This reduces the ability of vendors to tie a standard to the purchase of other products,. 2009) an...
projects.rajivshah.com
Rajiv Shah's Projects Blog ·
http://projects.rajivshah.com/blog
Some background and insights on my projects, feel free to contact me at @rajcs4. Rajiv Shah's Projects Blog. Building Worlds for Reinforcement Learning. Places reinforcement learning into the masses. It comes with a wealth of environments from the classic cart pole, board games, Atari, and now the new Universe. One area I like within the gym environments are the classic control problems (besides the fun of eating melon and poop. To begin, grab the repo for the OpenAI gym. Inside the repo, navigate to.
projects.rajivshah.com
RNN Addition (1st Grade) · Rajiv Shah's Projects Blog
http://projects.rajivshah.com/blog/2016/04/05/rnn_addition
Some background and insights on my projects, feel free to contact me at @rajcs4. Rajiv Shah's Projects Blog. RNN Addition (1st Grade). Ever since I ran across RNNs, they have intrigued me with their ability to learn. The best background is Denny Britz’s tutorial. Karpathy’s totally accessible and fun post on character-level language models. And Colah’s detailed descriptions of LSTMs. And the ECML/PKDD challenge. My first model was teaching an RNN to add between 5 to 15 single digit numbers. This woul...
projects.rajivshah.com
SportVu Analysis · Rajiv Shah's Projects Blog
http://projects.rajivshah.com/blog/2016/04/02/sportvu_analysis
Some background and insights on my projects, feel free to contact me at @rajcs4. Rajiv Shah's Projects Blog. This post shares some of the code that I have created for analyzing NBA SportVu. Data For background, the NBA SportVu data. Is motion data for the basketball and players taken 25 times a second. For a typical NBA game, this means about 2 million rows of data. The data for over 600 NBA games (first half of the 2015-2016 season) is available. The first is basic EDA. The next markdown, PBP. Using xgb...
projects.rajivshah.com
Shiny front end for Tensorflow demo · Rajiv Shah's Projects Blog
http://projects.rajivshah.com/blog/2016/04/01/tensorflow_shiny
Some background and insights on my projects, feel free to contact me at @rajcs4. Rajiv Shah's Projects Blog. Shiny front end for Tensorflow demo. I built a GUI front end for tensorflow from shiny, the code is available at Github. The shiny app allows trying different inputs, RNN cell types, and even optimizers. The results are shown with plots as well as a link to tensorboard. The app allows anyone to try out these models with a variety of modelling options. I have a live demo of this app. Take advantage...
projects.rajivshah.com
Potholes in Chicago
http://projects.rajivshah.com/potholes/index.html
A short analysis and visualization on how the city fixed 250,000 potholes reported in the last few years. Your browser does not support the video tag. I suggest you upgrade your browser. This page provides a some insights into the 250,000 potholes from 311 requests filled in the last few years by Chicago. Is a chart showing how many days it takes the city to fill a pothole. The vast majority are filled in less than a week after they have been reported. Finally, under movie. Rshah AT pobox.com.
projects.rajivshah.com
2D Outlier Analysis
http://projects.rajivshah.com/shiny/outlier
EM - can be slow to converge. Fuzzy kmeans - Gustafson and Kessel. Fuzzy k-means with polynomial fuzzifier. This site allows you to try a number of different outlier or anomaly detection algorithms. To use this page, choose your model, sample, and number of clusters. Outliers are marked with a star and cluster centers with an X. For more information on how to use this app, the models, the samples, and how to find outliers and anomalies, take a look at the tutorial section. Some things to try:. Kmeans (di...
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