Chris Hokamp chrishokamp

## 5_word2vec.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              1 star
            
          
                chrishokamp
                / 5_word2vec.ipynb
            
            
              Created
              May 13, 2016 15:11
            
              
                Example solution to Udacity Deep Learning course assignment 5 -- W2V CBOW
              
          
        Loading

      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## working_memorization_example.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                chrishokamp
                / working_memorization_example.ipynb
            
            
              Created
              August 6, 2015 16:12
            
              
                dataset shape is everything!
              
          
        Loading

      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## failed_memorization_example.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                chrishokamp
                / failed_memorization_example.ipynb
            
            
              Created
              August 6, 2015 00:17
            
              
                A toy example testing the ability (or lack thereof) of a vanilla RNN to memorize sequence information
              
          
        Loading

      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## wmt15_word_level_qe_evaluation.py
from __future__ import division, print_function
import codecs
from sklearn.metrics import f1_score
import numpy as np
from argparse import ArgumentParser

import logging

logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logger = logging.getLogger('wmt_eval_logger')

## hpt_rule_parse.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                chrishokamp
                / hpt_rule_parse.ipynb
            
            
              Created
              February 5, 2015 13:43
            
              
                simple parsing of hpt rules into preauthoring autocomplete templates
              
          
        Loading

      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## grab_images.py
#!/usr/bin/python
#Author: Chris Hokamp
#Spring 2013

import sys
import requests
from BeautifulSoup import BeautifulSoup
import re

if len(sys.argv) != 4:

## fracking-puns.txt
fraternize
sassafras's
frankish
franjo
refractor
frannie
infractions
frantically
frankfurters
frantically

## sync_with_upstream.sh
# cd into your top level directory ml-group/

# add a remote called 'upstream'
git remote add upstream https://github.com/dcumachinelearning/ml-group.git
# pull from upstream
git pull upstream master
# push the new commits from upstream to your fork on github
git push

## install_anaconda.sh
# install the anaconda python distribution
echo 'grabbing the distro, note that you need to accept the license...'
wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda-2.3.0-Linux-x86_64.sh
bash Anaconda*.sh
echo "all done, have a great day $USER"

## gensim_vector_space_notebook.json
{
 "metadata": {
  "name": "gensim_tutorial"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
	from __future__ import division, print_function
	import codecs
	from sklearn.metrics import f1_score
	import numpy as np
	from argparse import ArgumentParser

	import logging

	logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
	logger = logging.getLogger('wmt_eval_logger')
	#!/usr/bin/python
	#Author: Chris Hokamp
	#Spring 2013

	import sys
	import requests
	from BeautifulSoup import BeautifulSoup
	import re

	if len(sys.argv) != 4:
	fraternize
	sassafras's
	frankish
	franjo
	refractor
	frannie
	infractions
	frantically
	frankfurters
	frantically
	# cd into your top level directory ml-group/

	# add a remote called 'upstream'
	git remote add upstream https://github.com/dcumachinelearning/ml-group.git
	# pull from upstream
	git pull upstream master
	# push the new commits from upstream to your fork on github
	git push
	# install the anaconda python distribution
	echo 'grabbing the distro, note that you need to accept the license...'
	wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda-2.3.0-Linux-x86_64.sh
	bash Anaconda*.sh
	echo "all done, have a great day $USER"
	{
	"metadata": {
	"name": "gensim_tutorial"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{