h0bbel

# See http://help.ubuntu.com/community/UpgradeNotes for how to upgrade to
# newer versions of the distribution.
deb http://us.archive.ubuntu.com/ubuntu/ bionic main restricted
# deb-src http://us.archive.ubuntu.com/ubuntu/ bionic main restricted

## Major bug fix updates produced after the final release of the
## distribution.
deb http://us.archive.ubuntu.com/ubuntu/ bionic-updates main restricted
# deb-src http://us.archive.ubuntu.com/ubuntu/ bionic-updates main restricted

ppope

"""
preprocess-twitter.py

python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)"

Script for preprocessing tweets by Romain Paulus
with small modifications by Jeffrey Pennington
with translation to Python by Motoki Wu

Translation of Ruby script to create features for GloVe vectors for Twitter data.

nailo2c

# -*- coding: utf-8 -*-

import os
import argparse
import gym
import numpy as np
from itertools import count

import torch
import torch.nn as nn

zhanwenchen

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

hoffrocket

#!/usr/bin/env python

from multiprocessing import Process, Queue
import os
import sys
import time
import tornado.ioloop
import tornado.web

fabianp

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: 

    "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer 1999

    "Learning to rank from medical imaging data." Pedregosa, Fabian, et al., 
    Machine Learning in Medical Imaging 2012.

henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

diogojc

import numpy as np
from scipy.sparse import csc_matrix

def pageRank(G, s = .85, maxerr = .001):
    """
    Computes the pagerank for each of the n states.

    Used in webpage ranking and text summarization using unweighted
    or weighted transitions respectively.