wassname

"""
Here is a dice loss for keras which is smoothed to approximate a linear (L1) loss.
It ranges from 1 to 0 (no error), and returns results similar to binary crossentropy
"""

# define custom loss and metric functions 

from keras import backend as K

def dice_coef(y_true, y_pred, smooth=1):

magicznyleszek

CSS Selectors Cheatsheet

Hi! If you see an error or something is missing (like :focus-within for few years :P) please let me know ❤️

Element selectors

Element -- selects all h2 elements on the page

h2 {

hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

jonleighton

// Converts an ArrayBuffer directly to base64, without any intermediate 'convert to string then
// use window.btoa' step. According to my tests, this appears to be a faster approach:
// http://jsperf.com/encoding-xhr-image-data/5

/*
MIT LICENSE

Copyright 2011 Jon Leighton

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: