molpopgen

Problem

You want to use Jupyter remotely. X11 forwarding is too slow for this.

Solution

SSH port forwarding!

The recipe

KazChe

(from : https://simplifiedthinking.co.uk/2015/10/03/install-mqtt-server/ )

Installing Brew

The Mosquitto MQTT Server can be easily installed using Homebrew.  If it’s not installed on your system already, then a quick visit to the homepage will give you all you need to get going.  Homebrew is an OS X Package Manager for installing and updating non-Mac OS X utilities that are more commonly found in other variants of Linux.  To install the basic package manager run the following command.

ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Installing Mosquitto MQTT

bbengfort

import networkx as nx
import graph_tool as gt



def get_prop_type(value, key=None):
    """
    Performs typing and value conversion for the graph_tool PropertyMap class.
    If a key is provided, it also ensures the key is in a format that can be

joelouismarino

from __future__ import print_function
import imageio
from PIL import Image
import numpy as np
import keras

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD

atduskgreg

Interactive Machine Learning

Taught by Brad Knox at the MIT Media Lab in 2014. Course website. Lecture and visiting speaker notes.

• Power to the People: The Role of Humans in Interactive Machine Learning by Knox, Cakmak, Kulesza, Amershi, and Lau
• A Few Useful Things to Know about Machine Learning by Domingos
• Machine Learning that Matters by Wagstaff
• Beyond Concise and Colorful: Learning Intelligible Rules by Pazzani et al.
• [Designing Games with a Purpose] (https://www.cs.cmu.edu/~biglou/GWAP_CACM.pdf) by von Ahn and Dabbish
• [Human Model Evaluation in Interactive Supervised

ryanmaclean

#!/bin/bash

# Turn on job control to do more than one at a time
set -m

# Check to see if Homebrew is installed, and install it if it is not
command -v brew >/dev/null 2>&1 || { echo >&2 "You will need Homebrew to use this tool, installing now"; /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"; }

# Check to see if `netcat` is installed, install it if it is not
command -v netcat >/dev/null 2>&1 || { echo >&2 "You will also need netcat in order to use this tool, installing it now"; brew install netcat; }

meetps

Using Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

ktnyt

import argparse
import numpy as np
from chainer import Variable, FunctionSet, optimizers, cuda
import chainer.functions  as F
import cv2
import random
import cPickle as pickle
import sys

class ConvolutionalAutoencoder(FunctionSet):

ju2wheels

# Last updated: 08/24/2916
#
# Total instructions available: 18
#
# https://docs.docker.com/engine/reference/builder/
#
# You can use a .dockerignore file in the same context directory as
# your Dockerfile to ignore files in the context before sending them
# to the Docker daemon for building to speed up building.

lukehedger

$ ffmpeg -i input.mp4 -vcodec h264 -acodec mp2 output.mp4