nrollr

Install MongoDB on macOS Sierra

This procedure explains how to install MongoDB using Homebrew on macOS Sierra 10.12.
Official MongoDB install documentation: here

Install Homebrew

• Installing Homebrew is effortless, open Terminal and enter :
  $ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

iamaaditya

# author: Aaditya Prakash

# NVIDIA-SMI does not show the full command, and when it was launched and its RAM usage.
# PS does but it does but you need PIDs for that
# lsof /dev/nvidia gives PIDs but only for the user invoking it

# usage:
#        python programs_on_gpu.py

# Sample Output

alysonla

-- Active issues
-- Count of total active issues in the specified time frame
-- Source: githubarchive public data set via Google BigQuery http://githubarchive.org/

SELECT
	COUNT(DISTINCT JSON_EXTRACT_SCALAR(events.payload, '$.issue.id')) AS events_issue_count
FROM (SELECT * FROM TABLE_DATE_RANGE([githubarchive:day.],TIMESTAMP('2015-09-01'),TIMESTAMP('2016-08-31')))
 AS events

-- 10,723,492 active issues

logston

# Inspired by http://liuhongjiang.github.io/hexotech/2015/10/14/alembic-support-multiple-model-files/

def combine_metadata():
    from sqlalchemy import MetaData
    import models  # models file into which all models are imported

    model_classes = []
    for model_name in models.__all__:
        model_classes.append(getattr(models, model_name))

offchan42

ATTENTION PLEASE

This gist has been moved to https://github.com/offchan42/machine-learning-curriculum

Please see that repository instead because you can make pull requests there and later updates will be pushed there too.

===

Machine Learning Curriculum

mwmcode

// A [] queue of ppl and their weights
// B [] floors each person is going to
// M maximum floors of building
// X maximum people on elv
// Y maximum wight on elv

function solution(A, B, M, X, Y) {
	// if queue had one person and is going to a reachable floor
	if (A.length === 1 && B[0] <= M) {
		return 2;

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.

colah

Understanding LSTMs

• Japanese: qiita
• Russian: Alex Sosn
• Chinese: jianshu, csdn, csdn, Yangyu Chen

Restuta

/*  Sometimes it's pretty easy to run ito troubles with React ES6 components.
    Consider the following code: */

class EventStub extends Component {
  componentDidMount() {
    window.addEventListener('resize', this.onResize.bind(this)); //notice .bind
  }
  
  componentWillUnmount() {
    window.removeEventListener('resize', this.onResize.bind(this));

nrollr

Install MySQL on OS X El Capitan

Normally the installation of MySQL can be achieved with a single command, which executes a script provided by MacMiniVault : bash <(curl -Ls http://git.io/eUx7rg)

However, at the time of writing the script is not compatible with OS X El Capitan (10.11)

Install MySQL using Homebrew

An alternative to the aforementioned installation script is installing MySQL using Homebrew. This gist assumes you already have Homebrew installed, if not first read the article "Homebrew and El Capitan"

Make sure Homebrew has the latest formulae, so run brew update first