dideler

Machine Learning Online Resources

• Stanford's ML Class
• Notes by Alex Holehouse
• Notes by Thom M
• Internships

andrewschoen

# -*- coding: utf-8 -*-
import os
import StringIO
import hashlib

try:
    from boto.s3.connection import S3Connection
    from boto.s3.key import Key
except ImportError:
    raise ImproperlyConfigured, "Could not load Boto's S3 bindings."

datagrok

This article is now published on my website: Prefer Subshells for Context.

agramfort

"""
Non-Negative Garotte implementation with the scikit-learn
"""

# Author: Alexandre Gramfort <[email protected]>
#         Jaques Grobler (__main__ script) <[email protected]>
#
# License: BSD Style.

import numpy as np

fonnesbeck

mattions

================= Git: Crash course

How git is controlled

General config file

mblondel

# Mathieu Blondel, May 2012
# License: BSD 3 clause

import numpy as np

def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False):
    XX = np.sum(X * X, axis=1)[:, np.newaxis]
    YY = np.sum(Y ** 2, axis=1)[np.newaxis, :]
    distances = np.dot(X, Y.T)
    distances *= -2

jboner

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

miebach

1) Download nssm from http://nssm.cc/download/?page=download and extract nssm.exe, for example to %windir%\system32\

2) Install your service:

nssm.exe install my-node-service c:\programs\nodejes\node.exe c:\my\server.js

3) Start it using net start:

net start my-node-service

zachmayer

#Setup
rm(list = ls(all = TRUE))
setwd('path.to/cv.ts')

#Load Packages
require(forecast)
require(doParallel)
source('R/cv.ts.R')
source('R/forecast functions.R')