-
Go to https://developer.apple.com/downloads/index.action and search for "Command line tools" and choose the one for your Mac OSX
-
Go to http://brew.sh/ and enter the one-liner into the Terminal, you now have
brewinstalled (a better Mac ports) -
Install transmission-daemon with
brew install transmission -
Copy the startup config for launchctl with
ln -sfv /usr/local/opt/transmission/*.plist ~/Library/LaunchAgents
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| { | |
| "id": "/ctrlpkw", | |
| "groups": [ | |
| { | |
| "id": "/ctrlpkw/db", | |
| "apps": [ | |
| { | |
| "id": "/ctrlpkw/db/cassandra-seed", | |
| "constraints": [["hostname", "UNIQUE"]], | |
| "ports": [7199, 7000, 7001, 9160, 9042], |
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| import scalaz._ | |
| import Scalaz._ | |
| import scalaz.OptionT._ | |
| import com.twitter.util.Future | |
| /** | |
| * Simple example of Future chain, which only shows type level consistency. | |
| * Future chain can be implemented with optionT or simple flatMap chain. | |
| */ | |
| object Main extends App { |
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| package com.cloudwick.mapreduce.FileSystemAPI; | |
| import java.io.BufferedInputStream; | |
| import java.io.BufferedOutputStream; | |
| import java.io.File; | |
| import java.io.FileInputStream; | |
| import java.io.FileOutputStream; | |
| import java.io.IOException; | |
| import java.io.InputStream; | |
| import java.io.OutputStream; |
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| package com.twitter.algebird.caliper | |
| import com.google.caliper.{ Param, SimpleBenchmark } | |
| import com.google.common.hash.{ HashFunction, Hashing } | |
| /** | |
| * Benchmarks the hashing algorithms used by Count-Min sketch for CMS[BigInt]. | |
| * | |
| * The input values are generated ahead of time to ensure that each trial uses the same input (and that the RNG is not | |
| * influencing the runtime of the trials). |
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| import com.twitter.finagle.Http; | |
| import com.twitter.finagle.ListeningServer; | |
| import com.twitter.finagle.Service; | |
| import com.twitter.finagle.http.HttpMuxer; | |
| import com.twitter.util.Await; | |
| import com.twitter.util.Future; | |
| import java.net.InetSocketAddress; | |
| import static java.nio.charset.StandardCharsets.UTF_8; | |
| import static org.jboss.netty.buffer.ChannelBuffers.copiedBuffer; | |
| import org.jboss.netty.handler.codec.http.*; |
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| import scala.collection.immutable.TreeMap | |
| trait ToTreeMap[A] { | |
| type Result | |
| def treeMap(x: A): Result | |
| } | |
| trait LowerPriorityToTreeMap { | |
| implicit def plainMap[K, V](implicit ord: Ordering[K]): ToTreeMap[Map[K, V]] = |
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| #! /bin/bash | |
| sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E56151BF | |
| DISTRO=$(lsb_release -is | tr '[:upper:]' '[:lower:]') | |
| CODENAME=$(lsb_release -cs) | |
| echo "deb http://repos.mesosphere.io/${DISTRO} ${CODENAME} main" | sudo tee /etc/apt/sources.list.d/mesosphere.list | |
| sudo apt-get -y update --fix-missing | |
| sudo apt-get -y install mesosphere |
A checklist for designing and developing internet scale services, inspired by James Hamilton's 2007 paper "On Desgining and Deploying Internet-Scale Services."
- Does the design expect failures to happen regularly and handle them gracefully?
- Have we kept things as simple as possible?
- Feature Learning
- Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
- The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
- Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
- Scaling Learning Algorithms towards AI Benjio & LeCun
- A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson
- Deep Learning
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
- [Understanding