julionc

#!/usr/bin/env bash
# Things to do after install ArchLinux (2012.12.01)

pacman --noconfirm -S sudo

# Enabled archlinuxfr repo
arch=$(uname -m)
sudo cp /etc/pacman.conf /etc/pacman.conf.bak
echo "" >> /etc/pacman.conf
echo "[archlinuxfr]" >> /etc/pacman.conf

pithyless

Install Python

$ brew install readline sqlite gdbm
$ brew install python --universal --framework
$ python --version
Python 2.7

Symlinks...

kastnerkyle

# Authors: Kyle Kastner
# License: BSD 3-clause
import theano.tensor as T
import numpy as np
import theano


class rmsprop(object):
    """
    RMSProp with nesterov momentum and gradient rescaling

romainl

--langdef=less
--langmap=less:.less
--regex-less=/^[ \t&]*#([A-Za-z0-9_-]+)/\1/i,id,ids/
--regex-less=/^[ \t&]*\.([A-Za-z0-9_-]+)/\1/c,class,classes/
--regex-less=/^[ \t]*(([A-Za-z0-9_-]+[ \t\n,]+)+)\{/\1/t,tag,tags/
--regex-less=/^[ \t]*@media\s+([A-Za-z0-9_-]+)/\1/m,media,medias/
--regex-less=/^[ \t]*(@[A-Za-z0-9_-]+):/\1/v,variable,variables/
--regex-less=/\/\/[ \t]*(FIXME|TODO)[ \t]*\:*(.*)/\1/T,Tag,Tags/

--langdef=scss

GilLevi

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Convolutional neural networks for emotion classification from facial images as described in the following work:

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015

Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/

If you find our models useful, please add suitable reference to our paper in your work.

cashiwamochi

#include <opencv2/opencv.hpp>

#include <pcl/common/common_headers.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/visualization/pcl_visualizer.h>

#include <Eigen/Core>
#include <Eigen/LU>

liorshk

def log_run(gridsearch: sklearn.GridSearchCV, experiment_name: str, model_name: str, run_index: int, conda_env, tags={}):
    """Logging of cross validation results to mlflow tracking server
    
    Args:
        experiment_name (str): experiment name
        model_name (str): Name of the model
        run_index (int): Index of the run (in Gridsearch)
        conda_env (str): A dictionary that describes the conda environment (MLFlow Format)
        tags (dict): Dictionary of extra data and tags (usually features)

jzakiya

# This Crystal source file is a multiple threaded implementation to perform an
# extremely fast Segmented Sieve of Zakiya (SSoZ) to find Twin Primes <= N.

# Inputs are single values N, or ranges N1 and N2, of 64-bits, 0 -- 2^64 - 1.
# Output is the number of twin primes <= N, or in range N1 to N2; the last
# twin prime value for the range; and the total time of execution.

# Code originally developed on a System76 laptop with an Intel I7 6700HQ cpu,
# 2.6-3.5 GHz clock, with 8 threads, and 16GB of memory. Parameter tuning
# probably needed to optimize for other hardware systems (ARM, PowerPC, etc).

fyears

if you are using linux, unix, os x:

pip install -U setuptools
pip install -U pip

pip install numpy
pip install scipy
pip install matplotlib
#pip install PySide

bpierre

Switch To Vim For Good

NOTE: This guide has moved to https://github.com/bpierre/switch-to-vim-for-good

This guide is coming from an email I used to send to newcomers to Vim. It is not intended to be a complete guide, it is about how I switched myself.

My decision to switch to Vim has been made a long time ago. Coming from TextMate 1, I wanted to learn an editor that is Open Source (so I don’t lose my time learning a tool that can be killed), cross platform (so I can use it everywhere), and powerful enough (so I won’t regret TextMate). For these reasons, Vim has always been the editor I wanted to learn, but it took me several years before I did it in a way that works for me. I tried to switch progressively, using the Janus Vim distribution for a few months, then got back to using TextMate 2 for a time, waiting for the next attempt… here is what finally worked for me.

Original gist with comments: https://gist.github.com/bpierre/0a0025d348b6001394e0