Install Homebrew
http://mxcl.github.com/homebrew/
/usr/bin/ruby -e "$(curl -fsSL https://raw.github.com/gist/323731)"
brew help
cd ~
mate .bashrc
Install Homebrew
http://mxcl.github.com/homebrew/
/usr/bin/ruby -e "$(curl -fsSL https://raw.github.com/gist/323731)"
brew help
cd ~
mate .bashrc
http://s3.amazonaws.com/stanford_videos/cs229/videos/01.1-Introduction-Welcome.mp4 | |
http://s3.amazonaws.com/stanford_videos/cs229/videos/01.b-Introduction-WhatIsMachineLearning.mp4 | |
http://s3.amazonaws.com/stanford_videos/cs229/videos/01.3-Introduction-SupervisedLearning.mp4 | |
http://s3.amazonaws.com/stanford_videos/cs229/videos/01.d-Introduction-UnsupervisedLearning.mp4 | |
http://s3.amazonaws.com/stanford_videos/cs229/videos/02.1-LinearRegressionWithOneVariable-ModelRepresentation.mp4 | |
http://s3.amazonaws.com/stanford_videos/cs229/videos/02.2-LinearRegressionWithOneVariable-CostFunction.mp4 | |
http://s3.amazonaws.com/stanford_videos/cs229/videos/02.3-LinearRegressionWithOneVariable-CostFunctionIntuitionI.mp4 | |
http://s3.amazonaws.com/stanford_videos/cs229/videos/02.4-LinearRegressionWithOneVariable-CostFunctionIntuitionII.mp4 | |
http://s3.amazonaws.com/stanford_videos/cs229/videos/02.5-LinearRegressionWithOneVariable-GradientDescent.mp4 | |
http://s3.amazonaws.com/stanford_videos/cs229/videos/02.6-LinearRegressionWithOneVariabl |
import os, fnmatch | |
def locate(pattern, root=os.curdir): | |
'''Locate all files matching supplied filename pattern in and below | |
supplied root directory.''' | |
for path, dirs, files in os.walk(os.path.abspath(root)): | |
for filename in fnmatch.filter(files, pattern): | |
yield os.path.join(path, filename) | |
if __name__ == '__main__': |
Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
Essential blueprint of Play2 architecture is pretty simple and it should be easy to explain in a fairly short blog post. The framework can be understood progressively at different levels; each time having better exposure to some aspects of its design.
The core of Play2 is really small, surrounded by a fair amount of useful APIs, services and structure to make Web Programming tasks easier.
Basically, Play2 is an API that abstractly have the folllowing type