Shravankumar shravankumar147

Movies Recommendation:

MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html

How to use Pelican on GitHub Pages

Install on Ubuntu

Installing Python tools

sudo apt-get install python-setuptools

Moved

Now located at https://github.com/JeffPaine/beautiful_idiomatic_python.

Why it was moved

Github gists don't support Pull Requests or any notifications, which made it impossible for me to maintain this (surprisingly popular) gist with fixes, respond to comments and so on. In the interest of maintaining the quality of this resource for others, I've moved it to a proper repo. Cheers!

	from cvxopt import matrix as cvxmat, sparse, spmatrix
	from cvxopt.solvers import qp, options
	import numpy as np

	def quadprog(H, f, Aeq, beq, lb, ub):
	"""
	minimize:
	(1/2)x'Hx + f'x
	subject to:
	Aeq*x = beq

	#!/usr/bin/env PYTHONIOENCODING="utf-8" python
	"""
	A simple neural network learning the XOR function
	"""
	import tensorflow as tf
	sess = tf.InteractiveSession()

	# Desired input output mapping of XOR function:
	x_ = [[0, 0], [0, 1], [1, 0], [1, 1]] # input
	#labels=[0, 1, 1, 0] # output =>

	# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
	# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

	# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
	# where x' = [x \| 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
	# Similarly, for h * W_2 + b_2
	import tensorflow as tf
	import numpy as np
	from sklearn import datasets
	from sklearn.model_selection import train_test_split

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	# USAGE
	# python face_detection.py --image face1.jpg

	# import the necessary packages
	# from imutils import face_utils
	# import numpy as np
	import argparse
	import imutils
	import dlib
	import cv2

	# USAGE
	# python build_face_dataset.py --cascade haarcascade_frontalface_default.xml --output dataset/adrian

	# import the necessary packages
	from imutils.video import VideoStream
	import argparse
	import imutils
	import time
	import cv2
	import os

	http://nadbordrozd.github.io/blog/2016/05/20/text-classification-with-word2vec/
	https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html
	https://stackoverflow.com/questions/31321209/doc2vec-how-to-get-document-vectors
	http://linanqiu.github.io/2015/10/07/word2vec-sentiment/
	https://rare-technologies.com/doc2vec-tutorial/
	https://radimrehurek.com/gensim/models/doc2vec.html#gensim.models.doc2vec.TaggedDocument
	https://www.kaggle.com/tj2552/sentiment-classification-in-5-classes-doc2vec
	https://github.com/ibrahimsharaf/Doc2vec