ssahu

Keybase proof

I hereby claim:

• I am ssahu on github.
• I am sarthak (https://keybase.io/sarthak) on keybase.
• I have a public key ASDx8hldqw3vCE5Y0ep0KrmqglkAX3AdVKDcfF-G-rJthAo

To claim this, I am signing this object:

ssahu

#git config --list
filter.lfs.clean=git-lfs clean -- %f
filter.lfs.smudge=git-lfs smudge -- %f
filter.lfs.process=git-lfs filter-process
filter.lfs.required=true
user.name=Sarthak Sahu
[email protected]
filter.lfs.process=git-lfs filter-process
filter.lfs.required=true
filter.lfs.clean=git-lfs clean -- %f

ssahu

var intrinsic:Vector.<Number> = new Vector.<Number>(9, true);
var intrinsicInverse:Vector.<Number> = new Vector.<Number>(9, true);
		
var R:Vector.<Number> = new Vector.<Number>( 9, true );
var t:Vector.<Number> = new Vector.<Number>( 3, true );

// SVD routine
var svd:SVD = new SVD();

// input homography[9] - 3x3 Matrix

ssahu

#!/bin/bash


# Don't require you to constantly enter password for sudo:
sudo visudo
# In the bottom of the file, paste the following (without the `#`):
# paperspace ALL=(ALL) NOPASSWD: ALL
# Then press `ctl+o` then `enter` to save your changes, and `ctr+x` to exit nano

# Allow connection from your IP to any port- default seems to be just 22 (ssh)

ssahu

'''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

ssahu

'''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

ssahu

'''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

ssahu

boot2docker stop
boot2docker delete
sudo rm /usr/local/bin/docker
sudo rm /usr/local/bin/boot2docker
sudo rm /usr/local/share/boot2docker/boot2docker.iso
sudo rmdir /usr/local/share/boot2docker
rm -rf ~/.boot2docker
rm ~/.ssh/id_boot2docker*
sudo rm -f /private/var/db/receipts/io.boot2docker.*
sudo rm -f /private/var/db/receipts/io.boot2dockeriso.*

ssahu

instructions to set paths after installing mkl
file:///opt/intel/documentation_2016/en/ps2016/getstart_comp_mc.htm
sudo vi /opt/intel/composer_xe_2015/tbb/bin/tbbvars.sh
change == to '=='

disable system integration protection for el capitan: csrutil disable
to use the fallback library path
add the following to the bashrc/zshrc
export DYLD_FALLBACK_LIBRARY_PATH=/usr/local/lib:/usr/lib:/opt/intel/composer_xe_2015.2.132/compiler/lib:/opt/intel/composer_xe_2015.2.132/mkl/lib

ssahu

"""
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))
print '%d unique characters in data.' % (len(chars), )