grigory93

titanic_1tree = h2o.gbm(x = predictors, y = response, 
                        training_frame = titanicHex, 
                        ntrees = 1, min_rows = 1, sample_rate = 1, col_sample_rate = 1,
                        max_depth = 5,
                        # use early stopping once the validation AUC doesn't improve by at least 0.01%
                        # for 5 consecutive scoring events
                        stopping_rounds = 3, stopping_tolerance = 0.01, 
                        stopping_metric = "AUC", 
                        seed = 1)

vanyakosmos

// ==UserScript==
// @name         Github html preview
// @author       https://github.com/vaniakosmos
// @version      1.0
// @description  Shortcut for htmlpreview.github.io
// @match        https://github.com/**/*.html
// @grant        none
// @require http://code.jquery.com/jquery-3.2.1.min.js
// ==/UserScript==

JoshuaTPierce

Go to github
  Create new repository [don't need to initialize with the readme (can add later)]
    Go to R Studio
      File -> New Project -> Version Control -> Git
        Ctrl+V  repository URL from GitHub
          File -> New -> Markdown, enter Title, etc.
              In the Markdown window, change "output=html_document" to "output=github_document"
                Knit the document for the first time, will prompt you to save
                  Save as Title.rmd 
In the "git" tab of the R studio Environment window, you will notice that the knit produced:

shagunsodhani

Skip-Thought Vectors

Introduction

• The paper describes an unsupervised approach to train a generic, distributed sentence encoder.
• It also describes a vocabulary expansion method to encode words not seen at training time.
• Link to the paper

Skip-Thoughts