joshbuchea

Semantic Commit Messages

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional

Example

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

Nikolaj-K

In this video we come across about 50 online resources for category theory:

https://youtu.be/hEW42ARKNoE

I quickly comment on about 20 major ones. I link to the university sites, arXiv sites or Amazon page - most of the mentioned books are online available.

Here's another category theory list on github

https://github.com/prathyvsh/category-theory-resources

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.

rwightman

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair, _quadruple


class MedianPool2d(nn.Module):
    """ Median pool (usable as median filter when stride=1) module.
    

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}

apaszke

from graphviz import Digraph
import torch
from torch.autograd import Variable, Function

def iter_graph(root, callback):
    queue = [root]
    seen = set()
    while queue:
        fn = queue.pop()
        if fn in seen:

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

nmwsharp

Pretty print tables summarizing properties of tensor arrays in numpy, pytorch, jax, etc.

Now on pip! `pip install arrgh` https://github.com/nmwsharp/arrgh

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.