carlthome

johnloy

Electron apps

• Marp: Markdown presentation ecosystem.

• muryoimpl/slippr: markdown presentation app by Electron

• joe-re/cafe-pitch Markdown-driven presentation tool built on Electron.

Slide deck frameworks supporting Markdown source

swyoon

import numpy as np
import tensorflow as tf

__author__ = "Sangwoong Yoon"

def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
    """
    Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
    For supervised learning, feed training inputs to X and training labels to Y.
    For unsupervised learning, only feed training inputs to X, and feed None to Y.

pauloromeira

# ------------------------------------------------------------------------------
# tlp - Parameters for power saving
# See full explanation: http://linrunner.de/en/tlp/docs/tlp-configuration.html

# dir: /etc/default/tlp

# Hint: some features are disabled by default, remove the leading # to enable
# them.

# Set to 0 to disable, 1 to enable TLP.

alexhayes

Awesomely easy virtualenvs on OSX using pyenv and direnv

Never forget to activate that virtualenv or set that environment variable ever again...

Install

1. Install pyenv

    brew install pyenv
   

psycharo-zz

import threading

def _int64_feature(value):
  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

def _bytes_feature(value):
  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def _convert_example(rgb_path, label_path):
#   rgb_png = tf.gfile.GFile(rgb_path, 'rb').read()

weblancaster

#stop all containers:
docker kill $(docker ps -q)

#remove all containers
docker rm $(docker ps -a -q)

#remove all docker images
docker rmi $(docker images -q)

oinopion

-- Create a group
CREATE ROLE readaccess;

-- Grant access to existing tables
GRANT USAGE ON SCHEMA public TO readaccess;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO readaccess;

-- Grant access to future tables
ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON TABLES TO readaccess;

udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

blole

#!/usr/bin/env python2
import os
import gym
import sys
import random
import itertools
from time import time
from copy import copy
from math import sqrt, log