bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

matpalm

#!/usr/bin/env python
# see http://matpalm.com/blog/2015/03/28/theano_word_embeddings/
import theano
import theano.tensor as T
import numpy as np
import random

E = np.asarray(np.random.randn(6, 2), dtype='float32')
t_E = theano.shared(E)
t_idxs = T.ivector()

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

adilbaig

#!/bin/bash

# This is required for `notify-send` to work from within a cron.
# http://askubuntu.com/questions/298608/notify-send-doesnt-work-from-crontab/346580#346580
eval "export $(egrep -z DBUS_SESSION_BUS_ADDRESS /proc/$(pgrep -u $LOGNAME gnome-session)/environ)";

# syncAndWink
#
# Syncs all remotely-tracked branches on a git repo passed as first argument ($1). It also pulls any new branches
# and tags attached to the repo.

EderSantana

'''Trains a multi-output deep NN on the MNIST dataset using crossentropy and
   policy gradients (REINFORCE).
The goal of this example is twofold:
* Show how to use policy graidents for training
* Show how to use generators with multioutput models
# Policy graidients
This is a Reinforcement Learning technique [1] that trains the model
following the gradient of the logarithm of action taken scaled by the advantage
(reward - baseline) of that action.
# Generators

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

yoavg

Thoughts and some criticism on "Re-imagining Algorithmic Fairness in India and Beyond".

Yoav Goldberg, Jan 30, 2021

This new paper from Google Research Ethics Team (by Sambasivan, Arnesen, Hutchinson, Doshi, and Prabhakaran) touches on a very imortant topic: research (and supposedly also applied) work on algorithmic fairness---and more broadly AI-ethics---is US-centric[*], reflecting US subgroups, values, and methods. But AI is also applied elsewhere (for example, India). Do the methods and result developed for/in the US transfer? The answer is, of course, no, and the paper is doing a good job of showing it. If you are the kind of person who is impressed by the number of citations, this one has 220, a much higher number than another paper (not) from Google Research that became popular recently and which boasts many citations. I think this current paper (let's call it "the India Paper") is substantially more important, given that it raises a very serious issue that