Explore >> Select a destination
|
You are here 
|
aneesh.mataroa.blog | ||
| | | | |
tomaugspurger.net
|
|
| | | | | This work is supported by Anaconda, Inc. and the Data Driven Discovery Initiative from the Moore Foundation. This is part three of my series on scalable machine learning. Small Fit, Big Predict Scikit-Learn Partial Fit Parallel Machine Learning You can download a notebook of this post [here][notebook]. In part one, I talked about the type of constraints that push us to parallelize or distribute a machine learning workload. Today, we'll be talking about the second constraint, "I'm constrained by time, and would like to fit more models at once, by using all the cores of my laptop, or all the machines in my cluster". | |
| | | | |
highscalability.com
|
|
| | | | | HighScalability is What We Do: 454,400 : Number of Amazon servers; 45PB : Facebook Data War... | |
| | | | |
technicaldiscovery.blogspot.com
|
|
| | | | | Early Experience with Clusters My first real experience with cluster computing came in 1999 during my graduate school days at the Mayo Cl... | |
| | | | |
www.bailis.org
|
|
| | | |||
