amueller

import numpy as np
import matplotlib.pyplot as plt
from itertools import product
from sklearn.decomposition import RandomizedPCA
from sklearn.datasets import fetch_mldata
from sklearn.utils import shuffle

mnist = fetch_mldata("MNIST original")
X_train, y_train = mnist.data[:60000] / 255., mnist.target[:60000]

GaelVaroquaux

import numpy as np
import time

from sklearn import cluster
from sklearn import datasets


lfw = datasets.fetch_lfw_people()
X_lfw = lfw.data[:, :5]
eps = 8. # This choice of EPS gives 44 clusters

goldingn

# clear the workspace
rm(list = ls())

# load the relevant libraries

# install.packages(rCUR)
library(rCUR)  # for CUR decomposition
# install.packages(irlba)
library(irlba)  # for fast svd

syhw

"""
A deep neural network with or w/o dropout in one file.
"""

import numpy
import theano
import sys
import math
from theano import tensor as T
from theano import shared

amueller

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import fetch_mldata
from sklearn.decomposition import FastICA, PCA
from sklearn.cluster import KMeans

# fetch natural image patches
image_patches = fetch_mldata("natural scenes data")
X = image_patches.data

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:

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'

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.
//

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

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):