In this audio-only Becoming a Data Scientist Podcast Special Episode, I interview Dr. Ed Felten, Deputy U.S. Chief Technology Officer, about the Future of Artificial Intelligence (from The White House!).
Painted Cube Puzzle
A classic puzzle this week: The painted cube puzzle.
From Arrow to pandas at 10 Gigabytes Per Second
** Tue 27 December 2016
2017 Outlook： pandas, Arrow, Feather, Parquet, Spark, Ibis
** Tue 27 December 2016
Chuck-a-Luck
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There is a dice game you might have seen in casinos called Chuck-a-Luck (it is sometimes called Birdcage). The game is played by attempting to guess the outcome of three dice that are captive in a cage. |
| Bets are placed, then the cage containing the dice is inverted. When the dice come to rest their top faces are examined and any winning bets paid out. This article takes a look at the odds of the game.The game that is played in America is a variant of an Asian game called Sic-Bo. There are a variety of bets possible but the one we’ll look at first is the single number bet. |  |
Mathematically, what is the optimal pitch for a roof?
Disclaimer: This posting is just a light-weight excuse, in my last posting of this year, to perform a bit of calculus. I’m not seriously suggesting this is the technique engineers use to find the optimal pitches for rooves!
Assorted links
Avoiding overfitting in object detection problem
Recently I took part in AI Hackaton (2nd prize btw!) in Minsk with a pretty interesting challenge. My team tried to build a model that detects weeds at fields aerophotos. The photos were taken with a special multispectrum camera and looked like this:
Hamiltonian Monte Carlo explained
MCMC (Markov chain Monte Carlo) is a family of methods that are applied in computational physics and chemistry and also widely used in bayesian machine learning.
How-to： Automate Your sparklyr Environment with Cloudera Director
Since the launch of sparklyr, working with Apache Spark in Apache Hadoop has become much easier for R users. sparklyr contains a dplyr interface into Spark and allows users to leverage crucial machine learning algorithms from Spark MLlib and H2O Sparkling Water. This greatly reduces the barrier of entry for R users in adopting Spark as a tool for big data and should go a long way in enabling R workloads to migrate to Hadoop.