MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

tonyseek

Run with Python:

pip-2.7 install cffi
PYTHON=python2.7 sh go.sh

Run with PyPy:

pip-pypy install cffi
PYTHON=pypy sh go.sh

P7h

# Steps to build and install tmux from source.
# Takes < 25 seconds on EC2 env [even on a low-end config instance].

VERSION=2.7
sudo yum -y remove tmux
sudo yum -y install wget tar libevent-devel ncurses-devel
wget https://github.com/tmux/tmux/releases/download/${VERSION}/tmux-${VERSION}.tar.gz
tar xzf tmux-${VERSION}.tar.gz
rm -f tmux-${VERSION}.tar.gz
cd tmux-${VERSION}

FranciscoCanas

import numpy as np
import sys, os

# Edit the paths as needed:
caffe_root = '../caffe/'
sys.path.insert(0, caffe_root + 'python')

import caffe

# Path to your combined net prototxt files:

calstad

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

• Topology and Data by Gunnar Carlsson
• Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

• Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

synapticarbors

import numpy as np
import numba as nb

from numba import types
from numba.extending import overload_method


@overload_method(types.Array, 'take')
def array_take(arr, indices):
   if isinstance(indices, types.Array):

fubel

""" Radon Transform as described in Birkfellner, Wolfgang. Applied Medical Image Processing: A Basic Course. [p. 344] """
from scipy import misc
import numpy as np
import matplotlib.pyplot as plt

def discrete_radon_transform(image, steps):
    R = np.zeros((steps, len(image)), dtype='float64')
    for s in range(steps):
        rotation = misc.imrotate(image, -s*180/steps).astype('float64')
        R[:,s] = sum(rotation)

mjdietzx

#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda

lvzongting

#reference https://unix.stackexchange.com/questions/136371/how-to-download-a-folder-from-google-drive-using-terminal
#get cookie and code
wget --save-cookies cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=FILEID' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/Code: \1\n/p'

#download the file 
wget --load-cookies cookies.txt 'https://docs.google.com/uc?export=download&confirm=CODE_FROM_ABOVE&id=FILEID'

killeent

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.