Sergei Sokolov sergiks

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.

It is loaded by default by /Library/LaunchAgents/com.adobe.AdobeCreativeCloud.plist.

If you run

launchctl unload -w /Library/LaunchAgents/com.adobe.AdobeCreativeCloud.plist

Last major update: 25.08.2020

	'''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


	var net = require('net');

	// creates the server
	var server = net.createServer();

	//emitted when server closes ...not emitted until all connections closes.
	server.on('close',function(){
	console.log('Server closed !');
	});

	import { line, curve, curveCatmullRom } from "d3-shape";
	import { scaleTime, scaleLinear } from "d3-scale";
	import { axisBottom, axisLeft } from 'd3-axis';
	import { timeParse, isoFormat } from "d3-time-format";
	import { select } from "d3-selection";
	import { extent, max, min } from "d3-array";

	export default {
	line: line,
	scaleTime: scaleTime,

	#include <stdio.h>
	#include <stdlib.h>
	#include <stdint.h>
	#ifdef _MSC_VER
	#include <intrin.h> /* for rdtscp and clflush */
	#pragma optimize("gt",on)
	#else
	#include <x86intrin.h> /* for rdtscp and clflush */
	#endif

	license: gpl-3.0
	redirect: https://observablehq.com/@d3/d3-density-contours