Arun Reddy arunreddy

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Founder @ ProtoML

arunreddy / 0_reuse_code.js

Created June 18, 2014 03:29

Here are some things you can do with Gists in GistBox.

	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console

arunreddy / arch-linux-install

Created December 5, 2015 04:06 — forked from mattiaslundberg/arch-linux-install

Minimal instructions for installing arch linux on an UEFI system with full system encryption using dm-crypt and luks

	# Install ARCH Linux with encrypted file-system and UEFI
	# The official installation guide (https://wiki.archlinux.org/index.php/Installation_Guide) contains a more verbose description.

	# Download the archiso image from https://www.archlinux.org/
	# Copy to a usb-drive
	dd if=archlinux.img of=/dev/sdX bs=16M && sync # on linux

	# Boot from the usb. If the usb fails to boot, make sure that secure boot is disabled in the BIOS configuration.

	# Set swedish keymap

arunreddy / qsub.py

Last active May 17, 2016 15:20 — forked from astrofrog/qsub.py

Submitting jobs via qsub in Python

	import os
	import random
	import string
	import tempfile
	import subprocess

	def random_id(length=8):
	return ''.join(random.sample(string.ascii_letters + string.digits, length))

	TEMPLATE_SERIAL = """

arunreddy / clean_audio.sh

Created October 22, 2017 20:35 — forked from devoncrouse/clean_audio.sh

Using Sox (http://sox.sourceforge.net) to remove background noise and/or silence from audio files (individually, or in batch).

	# Create background noise profile from mp3
	/usr/bin/sox noise.mp3 -n noiseprof noise.prof

	# Remove noise from mp3 using profile
	/usr/bin/sox input.mp3 output.mp3 noisered noise.prof 0.21

	# Remove silence from mp3
	/usr/bin/sox input.mp3 output.mp3 silence -l 1 0.3 5% -1 2.0 5%

	# Remove noise and silence in a single command

arunreddy / pad_packed_demo.py

Created September 27, 2022 13:44 — forked from HarshTrivedi/pad_packed_demo.py

Minimal tutorial on packing (pack_padded_sequence) and unpacking (pad_packed_sequence) sequences in pytorch.

	import torch
	from torch import LongTensor
	from torch.nn import Embedding, LSTM
	from torch.autograd import Variable
	from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

	## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
	#
	# Step 1: Construct Vocabulary
	# Step 2: Load indexed data (list of instances, where each instance is list of character indices)