Naoya / nao-chan / naoya-tech naoyashiga

Software Engineer / YouTuber / Bilibili up

y-yu / inherit.md

Created August 9, 2012 06:57

JavaScriptの継承について

全然理解出来てなかったので調べてみた。

経緯

function f () {
	// Class
}

juno / mindbemding.md

Last active April 11, 2024 05:50

Summary of http://csswizardry.com/2013/01/mindbemding-getting-your-head-round-bem-syntax/ エッセンスだけ訳。翻訳の正しさは微妙。

haranicle / AppStoreScreenShotMaker.sh

Last active June 21, 2020 19:57

A shell script to create AppStore screen shots.

	#!/bin/sh

	# settings ==========

	# src file name
	fileNamePrefix="ScreenShotFileNameFor5.5inch"

	offsetFor3_5=20

	# dest directory name

cocopon / Easing.pde

Last active May 10, 2024 00:03

A class that brings Robert Penner's easing functions into Processing

	/*
	* Easing.pde - brings Robert Penner's easing functions into Processing
	* (c) 2015 cocopon.
	*
	* See the following to learn more about these famous functions:
	* http://www.robertpenner.com/easing/
	*
	* License:
	* http://www.robertpenner.com/easing_terms_of_use.html
	*/

cobalamin / UIViewKeyframeAnimationOptionsExtension.swift

Created June 24, 2015 08:33

Using all UIViewAnimationOptions as UIViewKeyframeAnimationOptions

	// This enables using all available UIViewAnimationOptions enum values as UIViewAnimationOptions in a Swifty way,
	// by brute-force bridging all values with an explicit extension to UIViewKeyframeAnimationOptions.
	//
	// Thus, code like
	//
	// let o1 : UIViewKeyframeAnimationOptions = .CalculationModeLinear
	// let o2 : UIViewAnimationOptions = .CurveLinear
	// let os : UIViewKeyframeAnimationOptions = [o1, UIViewKeyframeAnimationOptions(o2.rawValue)]
	//
	// becomes:

azu / _README.md

Last active April 8, 2020 00:53

node-init.sh

cburgdorf / xor_keras.py

Last active November 18, 2020 11:23

Comparing XOR between tensorflow and keras

	import numpy as np
	from keras.models import Sequential
	from keras.layers.core import Activation, Dense

	training_data = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
	target_data = np.array([[0],[1],[1],[0]], "float32")

	model = Sequential()
	model.add(Dense(32, input_dim=2, activation='relu'))
	model.add(Dense(1, activation='sigmoid'))