AI News, Month: March 2014
- On Friday, June 8, 2018
- By Read More
Month: March 2014
One of the most remarkable features of this year’s Strataconf was the almost universal use of IPython notebooks in presentations and tutorials.
This framework not only allows the speakers to demonstrate each step in the data science approach but also gives the audience an opportunity to do the same –
So, if you want to learn about predictions, modeling and large-scale data analysis, the following resources should give you a fantastic deep dive into these topics: 1) Mining the Social Web by Matthew A.
Russell If you want to learn how to automatically extract information from Twitter streams, Facebook fanpages, Google+ posts, Github accounts and many more information sources, this is the best resource to start.
In this notebook, Olivier explains how to set upand tune machine learning projects such as predictive modeling with the famous Titanic data-set on Kaggle.
The GraphLab library allows very fast access to large data structures with a special data frame format called the SFrame.
This notebook works on the Freebase movie database to find out whether the KevinBacon number really holds true or whether there are other actorsthat are more central in the movie universe.
Peter Norvig is not only the master mind behind the Google economy, teacher of a wonderful introduction to Python programmingat Udacity and author of many scientific papers on applied statistics and modeling, but he also seems to be the true nerd.
Who else would take a xkcd comic strip by the word and work out the regular expression matching patterns that provide a solution to the problem posed in the comic strip.
- On Wednesday, January 22, 2020
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