mblondel

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)

iamatypeofwalrus

What

Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.

What are we using? What do you need?

• An active AWS account. First time sign-ups are eligible for the free tier for a year
• One Micro Tier EC2 Instance
• With AWS we will use the stock Ubuntu Server AMI and customize it.
• Anaconda for Python.
• Coffee/Beer/Time

larsmans

#!/usr/bin/python
#
# K-means clustering using Lloyd's algorithm in pure Python.
# Written by Lars Buitinck. This code is in the public domain.
#
# The main program runs the clustering algorithm on a bunch of text documents
# specified as command-line arguments. These documents are first converted to
# sparse vectors, represented as lists of (index, value) pairs.

from collections import defaultdict

marcelcaraciolo

from numpy import loadtxt, zeros, ones, array, linspace, logspace
from pylab import scatter, show, title, xlabel, ylabel, plot, contour


#Evaluate the linear regression
def compute_cost(X, y, theta):
    '''
    Comput cost for linear regression
    '''
    #Number of training samples

thorikawa

#include <opencv2/opencv.hpp>
#include <vector>

using namespace cv;
using namespace std;

int main () {
  Mat img = imread("lena.jpg");
  CascadeClassifier cascade;
  if (cascade.load("haarcascade_frontalface_alt.xml")) {

alextp

class OnlineLearner(object):
    def __init__(self, **kwargs):
        self.last_misses = 0.
        self.iratio = 0.
        self.it = 1.
        self.l = kwargs["l"]
        self.max_ratio = -np.inf
        self.threshold = 500.

    def hinge_loss(self, vector, cls, weight):

kylemcdonald

# tested on AWS p2.xlarge August 29, 2018

# install CUDA
sudo apt-get update && sudo apt-get install wget -y --no-install-recommends
CUDA_URL="https://developer.nvidia.com/compute/cuda/9.2/Prod2/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64"
wget -c ${CUDA_URL} -O cuda.deb
sudo dpkg --install cuda.deb
sudo apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub
sudo apt-get update
sudo apt-get install -y cuda

spaghetti-source

// Read MNIST Database (handwritten digits)
//
// Usage: 
//   1. download 
//        train-images-idx3-ubyte.gz
//        train-labels-idx2-ubyte.gz
//      from
//        http://yann.lecun.com/exdb/mnist/
//      and extract them.
//

tariqmislam

=================================================================
SETTING UP SSHD AS A SERVICE FOR RUNNING HADOOP DAEMONS ON WINDOWS 7
=================================================================

Steps:

1. Download 'setup.exe' from Cygwin website
2. Right-click on 'setup.exe' 
3. Leave settings as they are, click through until you come to the plugin selection window
	3.1 - Make sure that the installation directory is 'C:\cygwin'

iandees

I've been interested in computer vision for a long time, but I haven't had any free time to make any progress until this holiday season. Over Christmas and the New Years I experimented with various methodologies in OpenCV to detect road signs and other objects of interest to OpenStreetMap. After some failed experiments with thresholding and feature detection, the excellent /r/computervision suggested using the dlib C++ module because it has more consistently-good documentation and the pre-built tools are faster.

After a day or two figuring out how to compile the examples, I finally made some progress:

Compiling dlib C++ on a Mac with Homebrew

1. Clone dlib from Github to your local machine: