wolever

Copyright (C) 2012 Yoshimasa Niwa

Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:

andkirby

#!/usr/bin/env bash
####################################################################################
# Slack Bash console script for sending messages.
####################################################################################
# Installation
#    $ curl -s https://gist.githubusercontent.com/andkirby/67a774513215d7ba06384186dd441d9e/raw --output /usr/bin/slack
#    $ chmod +x /usr/bin/slack
####################################################################################
# USAGE
# Send message to slack channel/user

r00tdaemon

Installation

pip3 install --user pyqt5  
sudo apt-get install python3-pyqt5  
sudo apt-get install pyqt5-dev-tools
sudo apt-get install qttools5-dev-tools

---

Configuring to run from terminal

bogsio

# https://nlpforhackers.io/named-entity-extraction/

import os
import string
import collections
import pickle
from collections import Iterable

from nltk.tag import ClassifierBasedTagger
from nltk.chunk import ChunkParserI, conlltags2tree, tree2conlltags

bgadrian

type Set struct {
	list map[int]struct{} //empty structs occupy 0 memory
}

func (s *Set) Has(v int) bool {
	_, ok := s.list[v]
	return ok
}

jayascript

Data Science Process

Steps to take for a comprehensive analysis.

Stage 1: Define

Project background.

Step 1.1: Describe.

deven96

deven96

Irio

FROM golang:1.12 as build

WORKDIR $GOPATH/src/github.com/Irio/wohnung
COPY scraper scraper
COPY main.go .

RUN go get -d -v ./...
RUN go install

FROM gcr.io/distroless/base

RileyLazarou

import copy

import numpy as np

class Organism():
    def __init__(self, dimensions, use_bias=True, output='softmax'):
        self.layers = []
        self.biases = []
        self.use_bias = use_bias
        self.output = self._activation(output)