Vikas Raunak vyraun

A very opinionated Python getting-started guide

Start by subscribing to Python Weekly mailinglist. Good overview of new projects, libraries, etc.
Depending on your programming experience, skip/skim/follow/study a course book. For free/online, you have for instance,
- Dive Into Python which is example-heavy & aimed at people with some programming background.
- How To Think Like a Computer Scientist starts more from scratch, and is also used in an introductory programming MIT OCW course for electrical engineering students if you want video lectures/excercises/exams.

Start by subscribing to Python Weekly mailinglist. Good overview of new projects, libraries, etc.
Depending on your programming experience, skip/skim/follow/study a course book. For free/online, you have for instance,
- Dive Into Python which is example-heavy & aimed at people with some programming background.
- How To Think Like a Computer Scientist starts more from scratch, and is also used in an introductory programming MIT OCW course for electrical engineering students if you want video lectures/excercises/exams.

	"""Visualize stability of stochastic gradient descent for finding a linear
	separator."""

	import numpy as np
	import matplotlib as mpl
	import matplotlib.pyplot as plt

	np.random.seed(1)
	mpl.rcParams['axes.linewidth'] = 0.0

	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
	% Matlab code to produce PCA animations shown here:
	% http://stats.stackexchange.com/questions/2691
	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

	% Static image

	clear all
	rng(42)

	"""
	TreeLSTM[1] implementation in Pytorch

	Based on dynet benchmarks :
	https://github.com/neulab/dynet-benchmark/blob/master/dynet-py/treenn.py
	https://github.com/neulab/dynet-benchmark/blob/master/chainer/treenn.py
	Other References:
	https://github.com/pytorch/examples/tree/master/word_language_model
	https://github.com/pfnet/chainer/blob/29c67fe1f2140fa8637201505b4c5e8556fad809/chainer/functions/activation/slstm.py
	https://github.com/stanfordnlp/treelstm

	"""
	TreeLSTM[1] implementation in Pytorch

	Based on dynet benchmarks :
	https://github.com/neulab/dynet-benchmark/blob/master/dynet-py/treenn.py
	https://github.com/neulab/dynet-benchmark/blob/master/chainer/treenn.py
	Other References:
	https://github.com/pytorch/examples/tree/master/word_language_model
	https://github.com/pfnet/chainer/blob/29c67fe1f2140fa8637201505b4c5e8556fad809/chainer/functions/activation/slstm.py
	https://github.com/stanfordnlp/treelstm

	#! /usr/bin/env python
	# wordcount.py: parse & return word frequency
	import sys, nltk

	f = open(sys.argv[1], 'rU')
	txt = f.read()
	f.close()

	tokens = nltk.word_tokenize(txt) # tokenize text
	clean_tokens = []