Mayukh Bhattacharyya mayukh18

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:

	model.zero_grad() # Reset gradients tensors
	for i, (inputs, labels) in enumerate(training_set):
	predictions = model(inputs) # Forward pass
	loss = loss_function(predictions, labels) # Compute loss function
	loss = loss / accumulation_steps # Normalize our loss (if averaged)
	loss.backward() # Backward pass
	if (i+1) % accumulation_steps == 0: # Wait for several backward steps
	optimizer.step() # Now we can do an optimizer step
	model.zero_grad() # Reset gradients tensors
	if (i+1) % evaluation_steps == 0: # Evaluate the model when we...

	#Author: Jacob Rust
	#Date: 7/8/2013
	#Description:This script organizes downloaded files into separate folders depending
	#on the file type. This was made for windows systems.
	#Download directory should be the first command line argument
	#New file types can be added to the dictionary in the main method

	import os
	import sys
	import hashlib