Elvis Saravia omarsar

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

UPDATE a fork of this gist has been used as a starting point for a community-maintained "awesome" list: machine-learning-with-ruby Please look here for the most up-to-date info!

Resources for Machine Learning in Ruby

Gems

liblinear-ruby: Ruby interface to LIBLINEAR using SWIG

Computing Resources

Operating system: Preferably Linux or MacOS. If you have Windows, things may crash unexpectedly (try installing a virtual machine if you need to)
RAM: Minimum 8GB
Disk space: Mininium 8GB

Software Requirements

Here is a list of the required programs and libraries necessary for this lab session. (Please install them before coming to our lab session on Tuesday; this will save us a lot of time, plus these are the same libraries you may need for your first assignment).

Python 3+ (Note: lab and assignment will be done strictly using Python 3)
Install latest version of Python 3

	import nltk

	with open('sample.txt', 'r') as f:
	sample = f.read()

	sentences = nltk.sent_tokenize(sample)
	tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
	tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
	chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)

	import math
	from text.blob import TextBlob as tb

	def tf(word, blob):
	return blob.words.count(word) / len(blob.words)

	def n_containing(word, bloblist):
	return sum(1 for blob in bloblist if word in blob)

	def idf(word, bloblist):

	from Population import Population
	from Individual import Individual
	from random import random, randint

	class Algorithm():

	#Constants
	Uniform_rate = 0.5
	Mutation_rate = 0.015
	Tournament_size = 5

	// layout file

	<body>
	<div class="container">

	<%= flash_messages %>

	<%= yield %>
	</div><!-- /container -->
	</body>

	<html>
	<head>
	<script src="https://www.gstatic.com/firebasejs/3.0.0/firebase.js"></script>
	<title>ZeroToApp</title>
	<style>
	#messages { width: 40em; border: 1px solid grey; min-height: 20em; }
	#messages img { max-width: 240px; max-height: 160px; display: block; }
	#header { position: fixed; top: 0; background-color: white; }
	.push { margin-bottom: 2em; }
	@keyframes yellow-fade { 0% {background: #f2f2b8;} 100% {background: none;} }

	# References
	# - https://www.tensorflow.org/versions/r0.10/tutorials/word2vec/index.html
	# - https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/examples/tutorials/word2vec/word2vec_basic.py

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import collections
	import math