swayson

Map	Action
<F1>	Causes Netrw to issue help
<cr>	Netrw will enter the directory or read the file
<del>	Netrw will attempt to remove the file/directory
-	Makes Netrw go up one directory
a	Toggles between normal display, hiding (suppress display of files matching g:netrw_list_hide) showing (display only files which match g:netrw_list_hide)
c	Make browsing directory the current directory
C	Setting the editing window
d	Make a directory

swayson

# Ask for the user password
# Script only works if sudo caches the password for a few minutes
sudo true

# Install kernel extra's to enable docker aufs support
# sudo apt-get -y install linux-image-extra-$(uname -r)

# Add Docker PPA and install latest version
# sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 36A1D7869245C8950F966E92D8576A8BA88D21E9
# sudo sh -c "echo deb https://get.docker.io/ubuntu docker main > /etc/apt/sources.list.d/docker.list"

swayson

#!/usr/bin/env bash
#Code adapted from https://gist.github.com/yangj1e/3641843c758201ebbc6c (Modified to Python3.5)

cd ~
#wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda2-2.4.0-Linux-x86_64.sh
wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda3-2.4.1-Linux-x86_64.sh
bash Anaconda3-2.4.1-Linux-x86_64.sh -b
echo 'PATH="/home/ubuntu/anaconda3/bin:$PATH"' >> .bashrc
. .bashrc

swayson

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

swayson

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

swayson

"""Kernel K-means"""

# Author: Mathieu Blondel <[email protected]>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

swayson

# script stolen from http://goo.gl/YbQyAQ

# install.packages("tm")
# install.packages("ggplot2")
# install.packages("lsa")
# install.packages("scatterplot3d")
#install.packages("SnowballC")
#if !(require('SnowballC')) then install.packages("SnowballC")
library(tm)
library(ggplot2)