Simon Šuster SimonSuster

## fairness in inida.md

      
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                yoavg
                / fairness in inida.md
            
            
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              January 5, 2025 10:43
            
          
    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

  
## cuda_11.8_installation_on_Ubuntu_22.04
#!/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

## mnist_rl_generator.py
'''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

## git-updater
#!/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.

## min-char-rnn.py
"""
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)

## README-Template.md

      
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                PurpleBooth
                / README-Template.md
            
            
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              October 31, 2025 20:49
            
              
                A template to make good README.md
              
          
    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


## theano_word_embeddings.py
#!/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()

## rank_metrics.py
"""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
	#!/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
	'''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
	#!/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.
	"""
	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)
	#!/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()
	"""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