haifengkao

# open the current folder in Finder's tab
function oft() {

  local folder_name=$1
  if ! [[ -d $1 ]]; then
    # it is a file, get the enclosing folder
    folder_name="$(dirname "$1")"
  fi

  # if no arguments are given, we use the current folder

junzis

sudo apt-get install libgeos-3.X.X
sudo apt-get install libgeos-dev
pip install --user https://github.com/matplotlib/basemap/archive/master.zip

madnight

{-# LANGUAGE OverloadedStrings #-}

import Data.Aeson
import Control.Applicative ((<$>), (<*>))
import Control.Monad (mzero)
import Network.HTTP.Conduit
import Data.ByteString.Lazy.Internal (ByteString(..))

data Item = Item { title        :: String
                 , url          :: String

ri-sh

# imports
import numpy as np
import cv2
import matplotlib.pyplot as plt


# The Hough Transform is a popular algorithm for detecting any shape that can
# be represented in a parametric mathmatical form in binary images. This
# usually means that images need to be thresholded or filtered prior to running

mattiaslundberg

Ansible playbook to setup HTTPS using Let's encrypt on nginx.

The Ansible playbook installs everything needed to serve static files from a nginx server over HTTPS.
The server pass A rating on [SSL Labs](https://www.ssllabs.com/).

To use:
 1. Install [Ansible](https://www.ansible.com/)
 2. Setup an Ubuntu 16.04 server accessible over ssh
 3. Create `/etc/ansible/hosts` according to template below and change example.com to your domain
 4. Copy the rest of the files to an empty directory (`playbook.yml` in the root of that folder and the rest in the `templates` subfolder)

fchollet

'''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

fchollet

'''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

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

wingedsheep

# import the gym stuff
import gym
# import other stuff
import random
import numpy as np
# import own classes
from deepq import DeepQ

env = gym.make('CartPole-v0')

olih

Processing JSON using jq

jq is useful to slice, filter, map and transform structured json data.

Installing jq

On Mac OS

brew install jq