noxx3xxon

import numpy as np
import cvxpy as cp
import itertools

# Problem data
global_indices = list(range(4))

# 0 = TOKEN-0
# 1 = TOKEN-1
# 2 = TOKEN-2

leonardreidy

# pip install Pillow if you don't already have it

# import image utilities
from PIL import Image

# import os utilities
import os

# define a function that rotates images in the current directory
# given the rotation in degrees as a parameter

azizur

The command line, in short…

wget -k -K -E -r -l 10 -p -N -F --restrict-file-names=windows -nH http://website.com/

…and the options explained

• -k : convert links to relative
• -K : keep an original versions of files without the conversions made by wget
• -E : rename html files to .html (if they don’t already have an htm(l) extension)
• -r : recursive… of course we want to make a recursive copy
• -l 10 : the maximum level of recursion. if you have a really big website you may need to put a higher number, but 10 levels should be enough.

IanColdwater

Mute these words in your settings here: https://twitter.com/settings/muted_keywords

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beesandbombs

// stics, or “sticks”

int[][] result;
float t, c;

float ease(float p) {
  return 3*p*p - 2*p*p*p;
}

float ease(float p, float g) {

beesandbombs

int[][] result;
float t, c;

float ease(float p) {
  return 3*p*p - 2*p*p*p;
}

float ease(float p, float g) {
  if (p < 0.5) 
    return 0.5 * pow(2*p, g);

beesandbombs

int[][] result;
float t, c;

float ease(float p) {
  return 3*p*p - 2*p*p*p;
}

float ease(float p, float g) {
  if (p < 0.5) 
    return 0.5 * pow(2*p, g);

beesandbombs

int[][] result;
float t, c;

float ease(float p) {
  return 3*p*p - 2*p*p*p;
}

float ease(float p, float g) {
  if (p < 0.5) 
    return 0.5 * pow(2*p, g);

beesandbombs

int[][] result;
float t, c;

float ease(float p) {
  return 3*p*p - 2*p*p*p;
}

float ease(float p, float g) {
  if (p < 0.5) 
    return 0.5 * pow(2*p, g);

shagunsodhani

The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.

Issues With Training Deep Neural Networks

Internal Covariate shift

Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.

It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.