mldeveloper01

#/usr/bin/python3
""" Demonstration of logging feature for a Flask App. """

from logging.handlers import RotatingFileHandler
from flask import Flask, request, jsonify
from time import strftime

__author__ = "@ivanleoncz"

import logging

mldeveloper01

import cv2
import sys
import numpy as np
 
camera = cv2.VideoCapture("video.avi")

# Setup BlobDetector
detector = cv2.SimpleBlobDetector_create()
params = cv2.SimpleBlobDetector_Params()
	 

mldeveloper01

Advanced Functional Programming with Scala - Notes

Copyright © 2016-2017 Fantasyland Institute of Learning. All rights reserved.

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

gentaiscool

I figured out how to build tensorflow from source in centOS. This process does not require any root access and you can do it anywhere. This will save your time and no need to worry much after this.

#What to prepare:

1. Java 8
2. Bazel
3. Tensorflow
4. CuDNN and CUDA toolkit (assume you have install them)

#Installation ##Bazel

aws-scripting-guy

# Get IAM Role name from Instance Profile Id
curl http://169.254.169.254/latest/meta-data/iam/info

# Get credentials
curl http://169.254.169.254/latest/meta-data/iam/security-credentials/<role-name>

# More info
http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-instance-metadata.html

subfuzion

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

a-paxton

# preliminaries
from pymongo import MongoClient
from nltk.corpus import stopwords
from string import ascii_lowercase
import pandas as pd
import gensim, os, re, pymongo, itertools, nltk, snowballstemmer

# set the location where we'll save our model
savefolder = '/data'

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

blackfalcon

Git-workflow vs feature branching

When working with Git, there are two prevailing workflows are Git workflow and feature branches. IMHO, being more of a subscriber to continuous integration, I feel that the feature branch workflow is better suited, and the focus of this article.

If you are new to Git and Git-workflows, I suggest reading the atlassian.com Git Workflow article in addition to this as there is more detail there than presented here.

I admit, using Bash in the command line with the standard configuration leaves a bit to be desired when it comes to awareness of state. A tool that I suggest using follows these instructions on setting up GIT Bash autocompletion. This tool will assist you to better visualize the state of a branc

mrdwab

stratified <- function(df, group, size, select = NULL, 
                       replace = FALSE, bothSets = FALSE) {
  if (is.null(select)) {
    df <- df
  } else {
    if (is.null(names(select))) stop("'select' must be a named list")
    if (!all(names(select) %in% names(df)))
      stop("Please verify your 'select' argument")
    temp <- sapply(names(select),
                   function(x) df[[x]] %in% select[[x]])