From a413ca68dd7b23082f965aea125b536a593f0f40 Mon Sep 17 00:00:00 2001
From: Daniel Smilkov <dsmilkov@gmail.com>
Date: Wed, 28 Mar 2018 11:03:32 -0400
Subject: [PATCH] Remove demos/homepage (#905)

---
 demos/homepage/Gemfile                   |  26 --
 demos/homepage/Gemfile.lock              | 205 --------------
 demos/homepage/_config.yml               |  33 ---
 demos/homepage/_includes/footer.html     |  32 ---
 demos/homepage/_includes/header.html     |  39 ---
 demos/homepage/_layouts/default.html     |  50 ----
 demos/homepage/_layouts/page.html        |  24 --
 demos/homepage/assets/style.css          | 325 -----------------------
 demos/homepage/assets/support.js         |  68 -----
 demos/homepage/homepage.ts               | 140 ----------
 demos/homepage/index.md                  | 306 ---------------------
 demos/homepage/preview.png               | Bin 212504 -> 0 bytes
 package.json                             |   6 +-
 scripts/build-npm.sh                     |   6 +-
 src/browser_util.ts                      |   2 +-
 src/environment.ts                       |   4 +-
 src/gradients.ts                         |  44 +--
 src/ops/array_ops.ts                     | 115 ++++----
 src/ops/binary_ops.ts                    |  88 +++---
 src/ops/concat.ts                        |  18 +-
 src/ops/matmul.ts                        |  12 +-
 src/ops/norm.ts                          |   4 +-
 src/ops/reduction_ops.ts                 |  56 ++--
 src/ops/reverse.ts                       |   4 +-
 src/ops/slice.ts                         |  12 +-
 src/ops/softmax.ts                       |   8 +-
 src/ops/transpose.ts                     |   4 +-
 src/ops/unary_ops.ts                     | 106 ++++----
 src/optimizers/optimizer_constructors.ts |  12 +-
 src/tensor.ts                            |   4 +-
 src/tracking.ts                          |  32 +--
 31 files changed, 268 insertions(+), 1517 deletions(-)
 delete mode 100644 demos/homepage/Gemfile
 delete mode 100644 demos/homepage/Gemfile.lock
 delete mode 100644 demos/homepage/_config.yml
 delete mode 100644 demos/homepage/_includes/footer.html
 delete mode 100644 demos/homepage/_includes/header.html
 delete mode 100644 demos/homepage/_layouts/default.html
 delete mode 100644 demos/homepage/_layouts/page.html
 delete mode 100644 demos/homepage/assets/style.css
 delete mode 100644 demos/homepage/assets/support.js
 delete mode 100644 demos/homepage/homepage.ts
 delete mode 100644 demos/homepage/index.md
 delete mode 100644 demos/homepage/preview.png

diff --git a/demos/homepage/Gemfile b/demos/homepage/Gemfile
deleted file mode 100644
index 8b53357a7e..0000000000
--- a/demos/homepage/Gemfile
+++ /dev/null
@@ -1,26 +0,0 @@
-source "https://rubygems.org"
-
-# Hello! This is where you manage which Jekyll version is used to run.
-# When you want to use a different version, change it below, save the
-# file and run `bundle install`. Run Jekyll with `bundle exec`, like so:
-#
-#     bundle exec jekyll serve
-#
-# This will help ensure the proper Jekyll version is running.
-# Happy Jekylling!
-
-# This is the default theme for new Jekyll sites. You may change this to anything you like.
-gem "minima", "~> 2.0"
-
-# If you want to use GitHub Pages, remove the "gem "jekyll"" above and
-# uncomment the line below. To upgrade, run `bundle update github-pages`.
-gem "github-pages", group: :jekyll_plugins
-
-# If you have any plugins, put them here!
-group :jekyll_plugins do
-   gem "jekyll-feed", "~> 0.6"
-end
-
-# Windows does not include zoneinfo files, so bundle the tzinfo-data gem
-#gem 'tzinfo-data', platforms: [:mingw, :mswin, :x64_mingw, :jruby]
-
diff --git a/demos/homepage/Gemfile.lock b/demos/homepage/Gemfile.lock
deleted file mode 100644
index 147c3924b8..0000000000
--- a/demos/homepage/Gemfile.lock
+++ /dev/null
@@ -1,205 +0,0 @@
-GEM
-  remote: https://rubygems.org/
-  specs:
-    activesupport (4.2.8)
-      i18n (~> 0.7)
-      minitest (~> 5.1)
-      thread_safe (~> 0.3, >= 0.3.4)
-      tzinfo (~> 1.1)
-    addressable (2.5.1)
-      public_suffix (~> 2.0, >= 2.0.2)
-    coffee-script (2.4.1)
-      coffee-script-source
-      execjs
-    coffee-script-source (1.12.2)
-    colorator (1.1.0)
-    ethon (0.10.1)
-      ffi (>= 1.3.0)
-    execjs (2.7.0)
-    faraday (0.12.2)
-      multipart-post (>= 1.2, < 3)
-    ffi (1.9.18)
-    forwardable-extended (2.6.0)
-    gemoji (3.0.0)
-    github-pages (146)
-      activesupport (= 4.2.8)
-      github-pages-health-check (= 1.3.5)
-      jekyll (= 3.4.5)
-      jekyll-avatar (= 0.4.2)
-      jekyll-coffeescript (= 1.0.1)
-      jekyll-default-layout (= 0.1.4)
-      jekyll-feed (= 0.9.2)
-      jekyll-gist (= 1.4.0)
-      jekyll-github-metadata (= 2.5.1)
-      jekyll-mentions (= 1.2.0)
-      jekyll-optional-front-matter (= 0.2.0)
-      jekyll-paginate (= 1.1.0)
-      jekyll-readme-index (= 0.1.0)
-      jekyll-redirect-from (= 0.12.1)
-      jekyll-relative-links (= 0.4.1)
-      jekyll-sass-converter (= 1.5.0)
-      jekyll-seo-tag (= 2.2.3)
-      jekyll-sitemap (= 1.0.0)
-      jekyll-swiss (= 0.4.0)
-      jekyll-theme-architect (= 0.0.4)
-      jekyll-theme-cayman (= 0.0.4)
-      jekyll-theme-dinky (= 0.0.4)
-      jekyll-theme-hacker (= 0.0.4)
-      jekyll-theme-leap-day (= 0.0.4)
-      jekyll-theme-merlot (= 0.0.4)
-      jekyll-theme-midnight (= 0.0.4)
-      jekyll-theme-minimal (= 0.0.4)
-      jekyll-theme-modernist (= 0.0.4)
-      jekyll-theme-primer (= 0.3.1)
-      jekyll-theme-slate (= 0.0.4)
-      jekyll-theme-tactile (= 0.0.4)
-      jekyll-theme-time-machine (= 0.0.4)
-      jekyll-titles-from-headings (= 0.2.0)
-      jemoji (= 0.8.0)
-      kramdown (= 1.13.2)
-      liquid (= 3.0.6)
-      listen (= 3.0.6)
-      mercenary (~> 0.3)
-      minima (= 2.1.1)
-      rouge (= 1.11.1)
-      terminal-table (~> 1.4)
-    github-pages-health-check (1.3.5)
-      addressable (~> 2.3)
-      net-dns (~> 0.8)
-      octokit (~> 4.0)
-      public_suffix (~> 2.0)
-      typhoeus (~> 0.7)
-    html-pipeline (2.6.0)
-      activesupport (>= 2)
-      nokogiri (>= 1.4)
-    i18n (0.8.6)
-    jekyll (3.4.5)
-      addressable (~> 2.4)
-      colorator (~> 1.0)
-      jekyll-sass-converter (~> 1.0)
-      jekyll-watch (~> 1.1)
-      kramdown (~> 1.3)
-      liquid (~> 3.0)
-      mercenary (~> 0.3.3)
-      pathutil (~> 0.9)
-      rouge (~> 1.7)
-      safe_yaml (~> 1.0)
-    jekyll-avatar (0.4.2)
-      jekyll (~> 3.0)
-    jekyll-coffeescript (1.0.1)
-      coffee-script (~> 2.2)
-    jekyll-default-layout (0.1.4)
-      jekyll (~> 3.0)
-    jekyll-feed (0.9.2)
-      jekyll (~> 3.3)
-    jekyll-gist (1.4.0)
-      octokit (~> 4.2)
-    jekyll-github-metadata (2.5.1)
-      jekyll (~> 3.1)
-      octokit (~> 4.0, != 4.4.0)
-    jekyll-mentions (1.2.0)
-      activesupport (~> 4.0)
-      html-pipeline (~> 2.3)
-      jekyll (~> 3.0)
-    jekyll-optional-front-matter (0.2.0)
-      jekyll (~> 3.0)
-    jekyll-paginate (1.1.0)
-    jekyll-readme-index (0.1.0)
-      jekyll (~> 3.0)
-    jekyll-redirect-from (0.12.1)
-      jekyll (~> 3.3)
-    jekyll-relative-links (0.4.1)
-      jekyll (~> 3.3)
-    jekyll-sass-converter (1.5.0)
-      sass (~> 3.4)
-    jekyll-seo-tag (2.2.3)
-      jekyll (~> 3.3)
-    jekyll-sitemap (1.0.0)
-      jekyll (~> 3.3)
-    jekyll-swiss (0.4.0)
-    jekyll-theme-architect (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-cayman (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-dinky (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-hacker (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-leap-day (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-merlot (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-midnight (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-minimal (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-modernist (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-primer (0.3.1)
-      jekyll (~> 3.3)
-    jekyll-theme-slate (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-tactile (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-theme-time-machine (0.0.4)
-      jekyll (~> 3.3)
-    jekyll-titles-from-headings (0.2.0)
-      jekyll (~> 3.3)
-    jekyll-watch (1.5.0)
-      listen (~> 3.0, < 3.1)
-    jemoji (0.8.0)
-      activesupport (~> 4.0)
-      gemoji (~> 3.0)
-      html-pipeline (~> 2.2)
-      jekyll (>= 3.0)
-    kramdown (1.13.2)
-    liquid (3.0.6)
-    listen (3.0.6)
-      rb-fsevent (>= 0.9.3)
-      rb-inotify (>= 0.9.7)
-    mercenary (0.3.6)
-    mini_portile2 (2.2.0)
-    minima (2.1.1)
-      jekyll (~> 3.3)
-    minitest (5.10.3)
-    multipart-post (2.0.0)
-    net-dns (0.8.0)
-    nokogiri (1.8.0)
-      mini_portile2 (~> 2.2.0)
-    octokit (4.7.0)
-      sawyer (~> 0.8.0, >= 0.5.3)
-    pathutil (0.14.0)
-      forwardable-extended (~> 2.6)
-    public_suffix (2.0.5)
-    rb-fsevent (0.10.2)
-    rb-inotify (0.9.10)
-      ffi (>= 0.5.0, < 2)
-    rouge (1.11.1)
-    safe_yaml (1.0.4)
-    sass (3.5.1)
-      sass-listen (~> 4.0.0)
-    sass-listen (4.0.0)
-      rb-fsevent (~> 0.9, >= 0.9.4)
-      rb-inotify (~> 0.9, >= 0.9.7)
-    sawyer (0.8.1)
-      addressable (>= 2.3.5, < 2.6)
-      faraday (~> 0.8, < 1.0)
-    terminal-table (1.8.0)
-      unicode-display_width (~> 1.1, >= 1.1.1)
-    thread_safe (0.3.6)
-    typhoeus (0.8.0)
-      ethon (>= 0.8.0)
-    tzinfo (1.2.3)
-      thread_safe (~> 0.1)
-    unicode-display_width (1.3.0)
-
-PLATFORMS
-  ruby
-
-DEPENDENCIES
-  github-pages
-  jekyll-feed (~> 0.6)
-  minima (~> 2.0)
-
-BUNDLED WITH
-   1.15.3
diff --git a/demos/homepage/_config.yml b/demos/homepage/_config.yml
deleted file mode 100644
index cb0c8718ed..0000000000
--- a/demos/homepage/_config.yml
+++ /dev/null
@@ -1,33 +0,0 @@
-# Welcome to Jekyll!
-#
-# This config file is meant for settings that affect your whole blog, values
-# which you are expected to set up once and rarely edit after that. If you find
-# yourself editing this file very often, consider using Jekyll's data files
-# feature for the data you need to update frequently.
-#
-# For technical reasons, this file is *NOT* reloaded automatically when you use
-# 'bundle exec jekyll serve'. If you change this file, please restart the server process.
-
-# Site settings
-# These are used to personalize your new site. If you look in the HTML files,
-# you will see them accessed via {{ site.title }}, {{ site.email }}, and so on.
-# You can create any custom variable you would like, and they will be accessible
-# in the templates via {{ site.myvariable }}.
-title: deeplearn.js
-baseurl: "" # the subpath of your site, e.g. /blog
-
-# Build settings
-markdown: kramdown
-theme: minima
-gems:
-  - jekyll-feed
-  - jekyll-redirect-from
-exclude:
-  - Gemfile
-  - Gemfile.lock
-  - node_modules/
-  - demos/node_modules/
-  - demos/bower_components/
-
-# deployment
-host: 0.0.0.0
diff --git a/demos/homepage/_includes/footer.html b/demos/homepage/_includes/footer.html
deleted file mode 100644
index eca84845e6..0000000000
--- a/demos/homepage/_includes/footer.html
+++ /dev/null
@@ -1,32 +0,0 @@
-<!-- Footer -->
-<footer class='mdl-mini-footer'>
-  <div class='mdl-mini-footer__left-section'>
-    <div class="mdl-logo">
-      <a href="https://www.google.com" target='_blank'>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 68 28" width="68" height="28">
-          <path fill="none" d="M0 0h68v28H0z"/>
-          <path fill="#9E9E9E" d="M8.34 18.32c-4.52 0-8.32-3.68-8.32-8.2s3.8-8.2 8.32-8.2c2.5 0 4.28.98 5.62 2.26l-1.58 1.58c-.96-.9-2.26-1.6-4.04-1.6-3.3 0-5.88 2.66-5.88 5.96s2.58 5.96 5.88 5.96c2.14 0 3.36-.86 4.14-1.64.64-.64 1.06-1.56 1.22-2.82H8.34V9.38h7.54c.08.4.12.88.12 1.4 0 1.68-.46 3.76-1.94 5.24-1.44 1.5-3.28 2.3-5.72 2.3zm19.46-5.28c0 3.04-2.38 5.28-5.3 5.28s-5.3-2.24-5.3-5.28c0-3.06 2.38-5.28 5.3-5.28s5.3 2.22 5.3 5.28zm-2.32 0c0-1.9-1.38-3.2-2.98-3.2s-2.98 1.3-2.98 3.2c0 1.88 1.38 3.2 2.98 3.2s2.98-1.32 2.98-3.2zm14.32 0c0 3.04-2.38 5.28-5.3 5.28s-5.3-2.24-5.3-5.28c0-3.06 2.38-5.28 5.3-5.28s5.3 2.22 5.3 5.28zm-2.32 0c0-1.9-1.38-3.2-2.98-3.2s-2.98 1.3-2.98 3.2c0 1.88 1.38 3.2 2.98 3.2s2.98-1.32 2.98-3.2zm13.84-4.96v9.48c0 3.9-2.3 5.5-5.02 5.5-2.56 0-4.1-1.72-4.68-3.12l2.02-.84c.36.86 1.24 1.88 2.66 1.88 1.74 0 2.82-1.08 2.82-3.1v-.76h-.08c-.52.64-1.52 1.2-2.78 1.2-2.64 0-5.06-2.3-5.06-5.26 0-2.98 2.42-5.3 5.06-5.3 1.26 0 2.26.56 2.78 1.18h.08v-.86h2.2zm-2.04 4.98c0-1.86-1.24-3.22-2.82-3.22-1.6 0-2.94 1.36-2.94 3.22 0 1.84 1.34 3.18 2.94 3.18 1.58 0 2.82-1.34 2.82-3.18zm6.41-10.58V18h-2.32V2.48h2.32zm9.41 12.3l1.8 1.2c-.58.86-1.98 2.34-4.4 2.34-3 0-5.24-2.32-5.24-5.28 0-3.14 2.26-5.28 4.98-5.28 2.74 0 4.08 2.18 4.52 3.36l.24.6-7.06 2.92c.54 1.06 1.38 1.6 2.56 1.6 1.18 0 2-.58 2.6-1.46zm-5.54-1.9l4.72-1.96c-.26-.66-1.04-1.12-1.96-1.12-1.18 0-2.82 1.04-2.76 3.08z"/>
-        </svg>
-      </a>
-    </div>
-    <ul class="mdl-mini-footer__link-list">
-      <li> <a href='https://www.google.ai/pair' target='_blank'>People + AI Research Initiative</a> </li>
-    </ul>
-  </div>
-  <div class='mdl-mini-footer__right-section'>
-    <ul class="mdl-mini-footer__link-list">
-      <li> <a href='https://pair-code.github.io' target='_blank'>PAIR Open Source</a> </li>
-      <li> <a href='https://github.com/PAIR-code' target='_blank'>Source on Github</a> </li>
-    </ul>
-  </div>
-</footer>
-
-<script>
- (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
- (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
- m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
- })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
- ga('create', 'UA-46457317-8', 'auto');
- ga('send', 'pageview');
-</script>
diff --git a/demos/homepage/_includes/header.html b/demos/homepage/_includes/header.html
deleted file mode 100644
index 852ea76eb2..0000000000
--- a/demos/homepage/_includes/header.html
+++ /dev/null
@@ -1,39 +0,0 @@
-<!-- Header -->
-<header class="mdl-layout__header mdl-layout--fixed-header">
-  <script>
-    if (location.protocol !== "https:" &&
-        location.hostname === 'deeplearnjs.org') {
-      location.protocol = "https:";
-    }
-  </script>
-  <div class="mdl-layout__header-row">
-    <!-- Title -->
-    <a class="main-title" href="{{ "/index.html" | relative_url }}"><span class="mdl-layout-title">deeplearn.js</span></a>
-    <!-- Add spacer, to align navigation to the right -->
-    <div class="mdl-layout-spacer"></div>
-    <!-- Navigation. We hide it in small screens. -->
-    <nav class="mdl-navigation mdl-layout--large-screen-only">
-      <a class="mdl-navigation__link" target='_blank' href="https://github.com/PAIR-code/deeplearnjs">Code</a>
-      <a class="mdl-navigation__link" href="{{ "/index.html#demos" | relative_url }}">Examples</a>
-      <a class="mdl-navigation__link" href="{{ "/#getting-started" | relative_url }}">Getting Started</a>
-      <!-- <a class="mdl-navigation__link" href="{{ "/docs/intro.html" | relative_url }}">Introduction</a> -->
-      <a class="mdl-navigation__link" href="{{ "/docs/tutorials/index.html" | relative_url }}">Tutorials</a>
-      <a class="mdl-navigation__link" href="{{ "/docs/api/index.html" | relative_url }}">API Reference</a>
-    </nav>
-  </div>
-</header>
-<!-- Drawer -->
-<div class="mdl-layout__drawer">
-  <span class="mdl-layout-title"><a href="{{ "/index.html" | relative_url }}">deeplearn.js</a></span>
-  <nav class="mdl-navigation">
-    <a class="mdl-navigation__link" target='_blank' href="https://github.com/PAIR-code/deeplearnjs">Code</a>
-    <a class="mdl-navigation__link" href="{{ "/index.html#demos" | relative_url }}">Examples</a>
-    <a class="mdl-navigation__link" href="{{ "/#getting-started" | relative_url }}">Getting Started</a>
-    <!-- <a class="mdl-navigation__link" href="{{ "/docs/intro.html" | relative_url }}">Introduction</a> -->
-    <a class="mdl-navigation__link" href="{{ "/docs/tutorials/index.html" | relative_url }}">Tutorials</a>
-    <a class="mdl-navigation__link" href="{{ "/docs/api/index.html" | relative_url }}">API Reference</a>
-
-    <a class="mdl-navigation__link" href="https://www.google.ai/pair" target='_blank'>People + AI Research Initiative</a>
-    <a class="mdl-navigation__link" href="https://pair-code.github.io" target='_blank'>PAIR Open Source</a>
-  </nav>
-</div>
diff --git a/demos/homepage/_layouts/default.html b/demos/homepage/_layouts/default.html
deleted file mode 100644
index 895f99b077..0000000000
--- a/demos/homepage/_layouts/default.html
+++ /dev/null
@@ -1,50 +0,0 @@
-<!-- Copyright 2017 Google Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-<!DOCTYPE html>
-<html>
-<head>
-  <meta charset="UTF-8">
-  <title>deeplearn.js</title>
-  <meta property="og:type" content="article"/>
-  <meta property="og:title" content="deeplearn.js"/>
-  <meta property="og:description" content="a hardware-accelerated machine intelligence library for the web">
-  <meta property="og:url" content="https://deeplearnjs.org"/>
-  <meta property="og:image" content="https://deeplearnjs.org/preview.png"/>
-
-  <meta name="twitter:card" value="summary_large_image">
-  <meta name="twitter:title" content="deeplearn.js">
-  <meta name="twitter:description" content="a hardware-accelerated machine intelligence library for the web">
-  <meta name="twitter:url" content="https://deeplearnjs.org">
-  <meta name="twitter:image" content="https://deeplearnjs.org/preview.png">
-  <meta name="twitter:image:width" content="835">
-  <meta name="twitter:image:height" content="547">
-
-  <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700" type="text/css">
-  <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
-  <link rel="stylesheet" href="{{ "/assets/main.css" | relative_url }}">
-  <link rel="stylesheet" href="https://code.getmdl.io/1.3.0/material.blue_grey-orange.min.css" />
-  <link rel="stylesheet" href="{{ "/assets/style.css" | relative_url }}">
-  <script src="https://code.getmdl.io/1.3.0/material.min.js"></script>
-</head>
-<body>
-  <div class="mdl-layout mdl-js-layout">
-    {% include header.html %}
-    <main class="mdl-layout__content">
-      {{ content }}
-      {% include footer.html %}
-    </main>
-  </div>
-</body>
-</html>
diff --git a/demos/homepage/_layouts/page.html b/demos/homepage/_layouts/page.html
deleted file mode 100644
index 8f69ea1b80..0000000000
--- a/demos/homepage/_layouts/page.html
+++ /dev/null
@@ -1,24 +0,0 @@
----
-layout: default
----
-<div class="mdl-grid">
-  <div class="mdl-layout-spacer"></div>
-  <div class="mdl-cell mdl-cell--8-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
-    <article class="post">
-      {{ content }}
-    </article>
-  </div>
-  <div class="mdl-layout-spacer"></div>
-  <div class="mdl-cell mdl-cell--3-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
-    {% assign pages_list = site.pages | sort: 'order' %}
-    <nav class="site-nav">
-      <ul>
-        {% for my_page in pages_list %}
-          {% if my_page.title and my_page.title != '_' %}
-            <li><a href="{{ my_page.url | relative_url }}">{{ my_page.title | escape }}</a></li>
-          {% endif %}
-        {% endfor %}
-      </ul>
-    </nav>
-  </div>
-</div>
diff --git a/demos/homepage/assets/style.css b/demos/homepage/assets/style.css
deleted file mode 100644
index 20200fc79f..0000000000
--- a/demos/homepage/assets/style.css
+++ /dev/null
@@ -1,325 +0,0 @@
-html {
-  width: 100%;
-  height: 100%;
-}
-body {
-  font-family: "Roboto", "Helvetica", "Arial", sans-serif;
-  margin: 0;
-  width: 100%;
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-  font-size: 16px;
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-.mdl-list__item a {
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-.mdl-layout__drawer {
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-.banner {
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-  min-height: 250px;
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-.banner-text {
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-  min-width: 300px;
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-.cppn-demo {
-  color:#fff;
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-  top: 0;
-  left: 0;
-}
-#disabled-demo {
-  margin: auto;
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-  top: 20%;
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-  padding: 32px;
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-  width: 100%;
-  height: calc(100% + 1px);
-  position: absolute;
-}
-.getmdl-select .mdl-icon-toggle__label{
-  float:right;
-  margin-top:-30px;
-  color:rgba(0,0,0,0.4);
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-/* Introduction */
-.intro-text {
-  margin: 32px auto 32px auto;
-}
-.mdl-grid {
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-.mdl-mini-footer {
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-.intro-headline {
-  line-height: 1.67em;
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-/* Responsive behavior */
-.intro-text > .mdl-cell--8-col-tablet {
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-.banner >.mdl-cell--8-col-tablet {
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-.highlight .err {
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-.site-nav ul li {
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-.featured-demo h1.mdl-card__title-text {
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-.demo-card .mdl-card__title {
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-.demo-card .mdl-card__title:before {
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-  font-weight: 200;
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-  letter-spacing: 0.5px;
-  background-color: rgba(0, 0, 0, .54);
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-#teachable-machine {
-  background: url('../demos/images/teachablemachine_preview.jpg') center / cover;
-}
-
-#perf-rnn {
-  background: url('../demos/images/performance_rnn_preview.jpg') center / cover;
-}
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-  background: url('../demos/images/model-builder.png') center / cover;
-}
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-  background: url('../demos/images/imagenet.png') center / cover;
-}
-
-#nnart {
-  background: url('../demos/images/nn-art.png') center / cover;
-}
-
-#benchmarks {
-  background: url('../demos/images/benchmark.png') center / cover;
-}
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-  background: url('../demos/images/playground_preview.png') center / cover;
-}
-
-.demo-card .mdl-card__title-text {
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-  z-index: 1;
-  font-weight: 400;
-}
-
-.mdl-typography--display-2, h1, h2, h3, h4 {
-  color: #414141;
-  font-weight: 300;
-}
-code {
-  background: none;
-}
-.highlighter-rouge .highlight {
-  background-color: #f5f5f5;
-  padding-bottom: 16px;
-  margin-bottom: 32px;
-}
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-  font-size: 40px;
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-p.intro-body {
-  font-weight: 300;
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-.mdl-mini-footer__link-list a {
-  color: rgb(158, 158, 158);
-}
-.mdl-mini-footer__link-list a:visited {
-  color: rgb(158, 158, 158);
-}
-
-#travis-badge {
-  display: none;
-}
diff --git a/demos/homepage/assets/support.js b/demos/homepage/assets/support.js
deleted file mode 100644
index 77b9334efb..0000000000
--- a/demos/homepage/assets/support.js
+++ /dev/null
@@ -1,68 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-function isWebGLEnabled() {
-  var canvas = document.createElement('canvas');
-
-  var attributes = {
-    alpha: false,
-    antialias: false,
-    premultipliedAlpha: false,
-    preserveDrawingBuffer: false,
-    depth: false,
-    stencil: false,
-    failIfMajorPerformanceCaveat: true
-  };
-  return null != (canvas.getContext('webgl', attributes) ||
-                  canvas.getContext('experimental-webgl', attributes));
-}
-
-function buildAndShowDialog(title, content) {
-  var dialogContainer = document.createElement('div');
-  dialogContainer.innerHTML = `
-    <dialog id="dialog" class="mdl-dialog">
-      <h4 class="mdl-dialog__title">${title}</h4>
-      <div class="mdl-dialog__content">
-        <p>${content}</p>
-      </div>
-    </dialog>
-  `;
-  document.body.appendChild(dialogContainer);
-  var dialog = document.getElementById('dialog');
-  dialog.style.width = '430px';
-  dialogPolyfill.registerDialog(dialog);
-  dialog.showModal();
-}
-
-function inializePolymerPage() {
-  document.addEventListener('WebComponentsReady', function(event) {
-    if (!isWebGLEnabled()) {
-      const title = `Check if hardware acceleration is enabled.`;
-      const content = `
-        Looks like your device is supported but settings aren't in place.
-        Please check if <b>WebGL</b> is enabled for your browser.
-
-        See: <a href='https://superuser.com/a/836833' target='_blank'>How can I enable WebGL in my browser?</a>
-      `;
-      buildAndShowDialog(title, content);
-    } else {
-      var bundleScript = document.createElement('script');
-      bundleScript.src = 'bundle.js';
-      document.head.appendChild(bundleScript);
-    }
-  });
-}
-inializePolymerPage();
diff --git a/demos/homepage/homepage.ts b/demos/homepage/homepage.ts
deleted file mode 100644
index 60971f821d..0000000000
--- a/demos/homepage/homepage.ts
+++ /dev/null
@@ -1,140 +0,0 @@
-/**
- * @license
- * Copyright 2017 Google Inc. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
-
-import {ENV} from 'deeplearn';
-import {ActivationFunction, CPPN} from '../nn-art/cppn';
-
-const inferenceCanvas =
-    document.querySelector('#inference') as HTMLCanvasElement;
-
-if (ENV.get('WEBGL_VERSION') >= 1) {
-  startCPPN();
-} else {
-  document.getElementById('disabled-demo-overlay').style.display = '';
-  inferenceCanvas.style.display = 'none';
-}
-
-function startCPPN() {
-  const DEFAULT_Z_SCALE = 1;
-  const NUM_NEURONS = 30;
-  const DEFAULT_NUM_LAYERS = 2;
-  const WEIGHTS_STDEV = 0.6;
-
-  const cppn = new CPPN(inferenceCanvas);
-
-  cppn.setActivationFunction('tanh');
-  cppn.setNumLayers(DEFAULT_NUM_LAYERS);
-  cppn.setZ1Scale(convertZScale(DEFAULT_Z_SCALE));
-  cppn.setZ2Scale(convertZScale(DEFAULT_Z_SCALE));
-  cppn.generateWeights(NUM_NEURONS, WEIGHTS_STDEV);
-  cppn.start();
-
-  const currentActivationFnElement =
-      document.querySelector('#activation-fn') as HTMLInputElement;
-  document.querySelector('#activation-selector')
-      .addEventListener(
-          // tslint:disable-next-line:no-any
-          'click', (event: any) => {
-            const activationFn =
-                (event.target as HTMLElement).getAttribute('data-val') as
-                ActivationFunction;
-            currentActivationFnElement.value = activationFn;
-            cppn.setActivationFunction(activationFn);
-          });
-
-  const layersSlider =
-      document.querySelector('#layers-slider') as HTMLInputElement;
-  const layersCountElement =
-      document.querySelector('#layers-count') as HTMLDivElement;
-  layersSlider.addEventListener('input', (event) => {
-    // tslint:disable-next-line:no-any
-    const numLayers = parseInt((event as any).target.value, 10);
-    layersCountElement.innerText = numLayers.toString();
-    cppn.setNumLayers(numLayers);
-  });
-  layersCountElement.innerText = DEFAULT_NUM_LAYERS.toString();
-
-  const z1Slider = document.querySelector('#z1-slider') as HTMLInputElement;
-  z1Slider.addEventListener('input', (event) => {
-    // tslint:disable-next-line:no-any
-    const z1Scale = parseInt((event as any).target.value, 10);
-    cppn.setZ1Scale(convertZScale(z1Scale));
-  });
-
-  const z2Slider = document.querySelector('#z2-slider') as HTMLInputElement;
-  z2Slider.addEventListener('input', (event) => {
-    // tslint:disable-next-line:no-any
-    const z2Scale = parseInt((event as any).target.value, 10);
-    cppn.setZ2Scale(convertZScale(z2Scale));
-  });
-
-  const randomizeButton =
-      document.querySelector('#random') as HTMLButtonElement;
-  randomizeButton.addEventListener('click', () => {
-    cppn.generateWeights(NUM_NEURONS, WEIGHTS_STDEV);
-    if (!playing) {
-      cppn.start();
-      requestAnimationFrame(() => {
-        cppn.stopInferenceLoop();
-      });
-    }
-  });
-
-  let playing = true;
-  const toggleButton = document.querySelector('#toggle') as HTMLButtonElement;
-  toggleButton.addEventListener('click', () => {
-    playing = !playing;
-    if (playing) {
-      toggleButton.innerHTML = 'STOP';
-      cppn.start();
-    } else {
-      toggleButton.innerHTML = 'START';
-      cppn.stopInferenceLoop();
-    }
-  });
-
-  let canvasOnScreenLast = true;
-  let scrollEventScheduled = false;
-  const mainElement = document.querySelector('main') as HTMLElement;
-  mainElement.addEventListener('scroll', () => {
-    if (!scrollEventScheduled) {
-      window.requestAnimationFrame(() => {
-        const canvasOnScreen = isCanvasOnScreen();
-        if (canvasOnScreen !== canvasOnScreenLast) {
-          if (canvasOnScreen) {
-            if (playing) {
-              cppn.start();
-            }
-          } else {
-            cppn.stopInferenceLoop();
-          }
-          canvasOnScreenLast = canvasOnScreen;
-        }
-        scrollEventScheduled = false;
-      });
-    }
-    scrollEventScheduled = true;
-  });
-
-  function isCanvasOnScreen() {
-    return mainElement.scrollTop < inferenceCanvas.offsetHeight;
-  }
-
-  function convertZScale(z: number): number {
-    return (103 - z);
-  }
-}
diff --git a/demos/homepage/index.md b/demos/homepage/index.md
deleted file mode 100644
index e78b532d85..0000000000
--- a/demos/homepage/index.md
+++ /dev/null
@@ -1,306 +0,0 @@
----
-layout: default
----
-<!-- Copyright 2017 Google Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================-->
-<!-- CPPN Demo Banner -->
-<div class="banner-cover" id='banner'>
-  <canvas id="inference"></canvas>
-  <div class="mdl-grid banner">
-    <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-    <div class="mdl-cell mdl-cell--6-col mdl-cell--8-col-tablet mdl-cell--4-col-phone banner-text">
-      <div class="mdl-typography--display-4">deeplearn.js</div>
-      <div class="mdl-typography--display-1" style="margin-left: 12px;">
-        a hardware-accelerated <br/>
-        machine intelligence<br/>
-        library for the web
-      </div>
-    </div>
-    <div class="mdl-layout-spacer  mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-    <div class="mdl-cell mdl-cell--3-col mdl-cell--8-col-tablet mdl-cell--4-col-phone cppn-controls">
-      <div class="mdl-textfield mdl-js-textfield mdl-textfield--floating-label getmdl-select getmdl-select__fix-height">
-        <input class="mdl-textfield__input" type="text" id="activation-fn" value="tanh" readonly tabIndex="-1">
-        <label for="activation-fn">
-          <i class="mdl-icon-toggle__label material-icons">keyboard_arrow_down</i>
-        </label>
-        <label for="activation-fn" class="mdl-textfield__label">Activation function</label>
-        <ul for="activation-fn" class="mdl-menu mdl-menu--bottom-left mdl-js-menu" id="activation-selector">
-          <li class="mdl-menu__item" data-val="tanh">tanh</li>
-          <li class="mdl-menu__item" data-val="sin">sin</li>
-          <li class="mdl-menu__item" data-val="relu">relu</li>
-          <li class="mdl-menu__item" data-val="step">step</li>
-        </ul>
-      </div>
-      <br>
-      <div>Number of layers:
-        <div id="layers-count" style="display: inline-block"></div>
-      </div>
-      <p style="width:200px">
-        <input class="mdl-slider mdl-js-slider" type="range" min="0" max="7" value="2" tabindex="1" id="layers-slider">
-      </p>
-      <div>z1 time</div>
-      <p style="width:200px">
-        <input class="mdl-slider mdl-js-slider" type="range" min="1" max="100" value="1" tabindex="2" id="z1-slider">
-      </p>
-      <div>z2 time</div>
-      <p style="width:200px">
-        <input class="mdl-slider mdl-js-slider" type="range" min="1" max="100" value="1" tabindex="3" id="z2-slider">
-      </p>
-      <button class="mdl-button mdl-js-button mdl-button--raised mdl-js-ripple-effect mdl-button--accent" type="button" id="random">randomize</button>
-      <button class="mdl-button mdl-js-button mdl-button--raised mdl-js-ripple-effect mdl-button--accent" type="button" id="toggle">stop</button>
-      <br><br>
-      <a href="http://blog.otoro.net/2016/03/25/generating-abstract-patterns-with-tensorflow/" target="_blank">What is a CPPN?</a>
-      <div id="disabled-demo-overlay" style="display: none">
-        <div id="disabled-demo">
-          Your device does not support WebGL, so we cannot show this demo. Please enable hardware acceleration or come back on a device that supports WebGL.
-        </div>
-      </div>
-    </div>
-    <div class="mdl-layout-spacer"></div>
-  </div>
-</div>
-<!-- Introduction Section -->
-<div class= "mdl-grid intro-text">
-  <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-  <div class= "mdl-cell mdl-cell--5-col mdl-cell--8-col-tablet mdl-cell-4-col-phone">
-    <p class='intro-headline mdl-typography--headline'><span class="deeplearn-shine">deeplearn.js</span> is an open-source library that brings performant machine learning building blocks to the web, allowing you to train neural networks in a browser or run pre-trained models in inference mode.</p>
-  </div>
-  <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-  <div class= "intro-text mdl-cell mdl-cell--4-col mdl-cell--8-col-tablet mdl-cell-4-col-phone">
-    <p class='intro-body mdl-typography--body-1'>We provide an API that closely mirrors
-    the TensorFlow eager API.<br><br><span class="deeplearn-shine">deeplearn.js</span>
-    was originally developed by the Google Brain PAIR team to build powerful
-    interactive machine learning tools for the browser. You can use the library
-    for everything from education, to model understanding, to art projects.</p>
-  </div>
-  <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-</div>
-<!-- Demo Section -->
-<div class="examples" id="demos">
-  <div class="section-title mdl-grid">
-    <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-    <p class="mdl-typography--display-2 mdl-cell mdl-cell--12-col">Examples</p>
-    <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-  </div>
-
-  <div class="featured-demo mdl-grid">
-    <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-    <div class="demo-card featured-demo mdl-card mdl-shadow--4dp feature-card mdl-cell mdl-cell--12-col">
-      <a href="demos/playground/examples.html" style="overflow: hidden">
-        <div class="mdl-card__title" id="playground">
-          <h1 class="mdl-card__title-text" class="mdl-card__title-text">Interactive Playground</h1>
-        </div>
-      </a>
-      <div class="mdl-card__supporting-text">Play with deeplearn.js code in the browser with no installs</div>
-      <div class="mdl-card__actions mdl-card--border">
-        <a href="demos/playground/examples.html">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Start playing
-          </button>
-        </a>
-      </div>
-    </div>
-  </div>
-  <!-- Featured Card -->
-  <div class="featured-demo mdl-grid">
-    <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-    <div class="demo-card featured-demo mdl-card mdl-shadow--4dp feature-card mdl-cell mdl-cell--6-col">
-      <a href="demos/performance_rnn">
-        <div class="mdl-card__title" id="perf-rnn">
-          <h1 class="mdl-card__title-text">Performance RNN</h1>
-        </div>
-      </a>
-      <div class="mdl-card__supporting-text">Enjoy a real-time piano performance by a neural network</div>
-      <div class="mdl-card__actions mdl-card--border">
-        <a href="demos/performance_rnn">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Demo
-          </button>
-        </a>
-        <a href="https://github.com/pair-code/deeplearnjs/tree/master/demos/performance_rnn">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Code
-          </button>
-        </a>
-      </div>
-    </div>
-    <div class="demo-card featured-demo mdl-card mdl-shadow--4dp feature-card mdl-cell mdl-cell--6-col">
-      <a href="https://teachablemachine.withgoogle.com/">
-        <div class="mdl-card__title" id="teachable-machine">
-          <h1 class="mdl-card__title-text">Teachable Machine</h1>
-        </div>
-      </a>
-      <div class="mdl-card__supporting-text">Explore machine learning, no coding required!</div>
-      <div class="mdl-card__actions mdl-card--border">
-        <a href="https://teachablemachine.withgoogle.com/">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Demo
-          </button>
-        </a>
-        <a href="https://github.com/googlecreativelab/teachable-machine">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Code
-          </button>
-        </a>
-      </div>
-    </div>
-    <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-  </div>
-  <!-- Demo Carousel -->
-  <div class="demo-carousel mdl-grid">
-    <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-    <div class="demo-card mdl-card mdl-shadow--2dp square-card mdl-cell mdl-cell--3-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
-      <a href="demos/model-builder/">
-        <div class="mdl-card__title" id="model-builder">
-          <span class="mdl-card__title-text">Model Builder</span>
-        </div>
-      </a>
-      <div class="mdl-card__supporting-text">Build a neural network in your browser, without code!</div>
-      <div class="mdl-card__actions mdl-card--border">
-        <a href="demos/model-builder/">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Demo
-          </button>
-        </a>
-        <a href="https://github.com/PAIR-code/deeplearnjs/tree/master/demos/model-builder/">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Code
-          </button>
-        </a>
-      </div>
-    </div>
-    <div class="demo-card mdl-card mdl-shadow--2dp square-card mdl-cell mdl-cell--3-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
-      <a href="demos/imagenet/">
-        <div class="mdl-card__title" id="webcam">
-          <span class="mdl-card__title-text">Webcam Imagenet</span>
-        </div>
-      </a>
-      <div class="mdl-card__supporting-text">Squeezenet running in the browser</div>
-      <div class="mdl-card__actions mdl-card--border">
-        <a href="demos/imagenet/">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Demo
-          </button>
-        </a>
-        <a href="https://github.com/PAIR-code/deeplearnjs/tree/master/demos/imagenet">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Code
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-        </a>
-      </div>
-    </div>
-    <div class="demo-card mdl-card mdl-shadow--2dp square-card mdl-cell mdl-cell--3-col mdl-cell--8-col-tablet mdl-cell--4-col-phone ">
-      <a href="demos/nn-art/">
-        <div class="mdl-card__title" id="nnart">
-          <span class="mdl-card__title-text">NNArt</span>
-        </div>
-      </a>
-      <div class="mdl-card__supporting-text">Play with an animating CPPN</div>
-      <div class="mdl-card__actions mdl-card--border">
-        <a href="demos/nn-art/">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Demo
-          </button>
-        </a>
-        <a href="https://github.com/PAIR-code/deeplearnjs/tree/master/demos/nn-art">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Code
-          </button>
-        </a>
-      </div>
-    </div>
-    <div class="demo-card mdl-card mdl-shadow--2dp square-card mdl-cell mdl-cell--3-col mdl-cell--8-col-tablet mdl-cell--4-col-phone">
-      <a href="demos/benchmarks/">
-        <div class="mdl-card__title" id="benchmarks">
-          <span class="mdl-card__title-text">Benchmarks</span>
-        </div>
-      </a>
-      <div class="mdl-card__supporting-text">Test the library's performance</div>
-      <div class="mdl-card__actions mdl-card--border">
-        <a href="demos/benchmarks/">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Demo
-          </button>
-        </a>
-        <a href=" https://github.com/PAIR-code/deeplearnjs/tree/master/demos/benchmarks">
-          <button class="mdl-button mdl-button--raised mdl-button--colored mdl-js-button mdl-button--primary mdl-js-ripple-effect">
-            Code
-          </button>
-        </a>
-      </div>
-    </div>
-    <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-  </div>
-</div>
-
-
-<div class="mdl-grid">
-  <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-  <div class="mdl-cell mdl-cell--10-col">
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-<div class='mdl-grid'>
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-  <div class="mdl-cell mdl-cell--10-col">
-    <h2 class= 'mdl-card__title-text'>Acknowledgements</h2>
-  </div>
-  <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-</div>
-
-<div class= "mdl-grid ack">
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-  <div class= "mdl-cell mdl-cell--5-col mdl-cell--8-col-tablet mdl-cell-4-col-phone">
-    <p class="intro-body mdl-typography--body-1">
-      <span class="deeplearn-shine">deeplearn.js</span> was originally developed by
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-    </p>
-    <script>
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-        elem.innerText = 'Daniel Smilkov';
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-      }
-
-      var author1 = document.getElementById('author1');
-      var author2 = document.getElementById('author2');
-      if (Math.random() > .5) {
-        daniel(author1);
-        nikhil(author2);
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-        nikhil(author1);
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-  <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-  <div class= "ack mdl-cell mdl-cell--4-col mdl-cell--8-col-tablet mdl-cell-4-col-phone">
-    <p class='intro-body mdl-typography--body-1'>
-      We would like to acknowledge Chi Zeng, David Farhi, Mahima Pushkarna,
-      Lauren Hannah-Murphy, Minsuk (Brian) Kahng, James Wexler, Martin Wattenberg,
-      Fernanda Viégas, Greg Corrado, Jeff Dean for their tremendous help, and the
-      Google Brain team for providing support for the project.
-    </p>
-  </div>
-  <div class="mdl-layout-spacer mdl-cell--hide-tablet mdl-cell--hide-phone"></div>
-</div>
-<script src="bundle.js"></script>
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diff --git a/package.json b/package.json
index ab6bb36a34..db9c270187 100644
--- a/package.json
+++ b/package.json
@@ -1,11 +1,11 @@
 {
-  "name": "deeplearn",
+  "name": "@tensorflow/tfjs-core",
   "version": "0.6.0-alpha7",
   "description": "Hardware-accelerated JavaScript library for machine intelligence",
   "private": false,
   "main": "dist/index.js",
-  "jsdelivr": "dist/deeplearn.min.js",
-  "unpkg": "dist/deeplearn.min.js",
+  "jsdelivr": "dist/tf-core.min.js",
+  "unpkg": "dist/tf-core.min.js",
   "types": "dist/index.d.ts",
   "repository": {
     "type": "git",
diff --git a/scripts/build-npm.sh b/scripts/build-npm.sh
index 807c39ac9b..4a93f7428f 100755
--- a/scripts/build-npm.sh
+++ b/scripts/build-npm.sh
@@ -19,7 +19,7 @@ set -e
 rimraf dist/
 yarn
 tsc --sourceMap false
-browserify --standalone dl src/index.ts -p [tsify] > dist/deeplearn.js
-uglifyjs dist/deeplearn.js -c -m -o dist/deeplearn.min.js
-echo "Stored standalone library at dist/deeplearn(.min).js"
+browserify --standalone tfc src/index.ts -p [tsify] > dist/tf-core.js
+uglifyjs dist/tf-core.js -c -m -o dist/tf-core.min.js
+echo "Stored standalone library at dist/tf-core(.min).js"
 npm pack
diff --git a/src/browser_util.ts b/src/browser_util.ts
index b84058cbbc..26e5c9fbc3 100644
--- a/src/browser_util.ts
+++ b/src/browser_util.ts
@@ -21,7 +21,7 @@ export class BrowserUtil {
    * Returns a promise that resolve when a requestAnimationFrame has completed.
    *
    * This is simply a sugar method so that users can do the following:
-   * `await dl.nextFrame();`
+   * `await tf.nextFrame();`
    */
   @doc({heading: 'Performance', subheading: 'Timing'})
   static nextFrame(): Promise<void> {
diff --git a/src/environment.ts b/src/environment.ts
index e6301624d5..b96c53b774 100644
--- a/src/environment.ts
+++ b/src/environment.ts
@@ -222,7 +222,7 @@ export class Environment {
    *
    * @param backendType The backend type. Currently supports `'webgl'|'cpu'`.
    * @param safeMode Defaults to false. In safe mode, you are forced to
-   *     construct tensors and call math operations inside a `dl.tidy()` which
+   *     construct tensors and call math operations inside a `tidy()` which
    *     will automatically clean up intermediate tensors.
    */
   @doc({heading: 'Environment'})
@@ -256,7 +256,7 @@ export class Environment {
    * - `unreliable`: `Optional` `boolean`:
    *    - On WebGL, not present (always reliable).
    *    - On CPU, true. Due to automatic garbage collection, these numbers
-   *     represent undisposed tensors, i.e. not wrapped in `dl.tidy()`, or
+   *     represent undisposed tensors, i.e. not wrapped in `tidy()`, or
    *     lacking a call to `tensor.dispose()`.
    */
   @doc({heading: 'Performance', subheading: 'Memory'})
diff --git a/src/gradients.ts b/src/gradients.ts
index 847d779245..e26ee83601 100644
--- a/src/gradients.ts
+++ b/src/gradients.ts
@@ -53,21 +53,21 @@ export class Gradients {
    * // f(x) = x ^ 2
    * const f = x => x.square();
    * // f'(x) = 2x
-   * const g = dl.grad(f);
+   * const g = tf.grad(f);
    *
-   * const x = dl.tensor1d([2, 3]);
+   * const x = tf.tensor1d([2, 3]);
    * g(x).print();
    * ```
    *
    * ```js
    * // f(x) = x ^ 3
-   * const f = x => x.pow(dl.scalar(3, 'int32'));
+   * const f = x => x.pow(tf.scalar(3, 'int32'));
    * // f'(x) = 3x ^ 2
-   * const g = dl.grad(f);
+   * const g = tf.grad(f);
    * // f''(x) = 6x
-   * const gg = dl.grad(g);
+   * const gg = tf.grad(g);
    *
-   * const x = dl.tensor1d([2, 3]);
+   * const x = tf.tensor1d([2, 3]);
    * gg(x).print();
    * ```
    *
@@ -111,10 +111,10 @@ export class Gradients {
    * // f(a, b) = a * b
    * const f = (a, b) => a.mul(b);
    * // df / da = b, df / db = a
-   * const g = dl.grads(f);
+   * const g = tf.grads(f);
    *
-   * const a = dl.tensor1d([2, 3]);
-   * const b = dl.tensor1d([-2, -3]);
+   * const a = tf.tensor1d([2, 3]);
+   * const b = tf.tensor1d([-2, -3]);
    * const [da, db] = g([a, b]);
    * console.log('da');
    * da.print();
@@ -150,7 +150,7 @@ export class Gradients {
   }
 
   /**
-   * Like `dl.grad`, but also returns the value of `f()`. Useful when `f()`
+   * Like `grad`, but also returns the value of `f()`. Useful when `f()`
    * returns a metric you want to show.
    *
    * The result is a rich object with the following properties:
@@ -161,9 +161,9 @@ export class Gradients {
    * // f(x) = x ^ 2
    * const f = x => x.square();
    * // f'(x) = 2x
-   * const g = dl.valueAndGrad(f);
+   * const g = tf.valueAndGrad(f);
    *
-   * const x = dl.tensor1d([2, 3]);
+   * const x = tf.tensor1d([2, 3]);
    * const {value, grad} = g(x);
    *
    * console.log('value');
@@ -206,10 +206,10 @@ export class Gradients {
    * // f(a, b) = a * b
    * const f = (a, b) => a.mul(b);
    * // df/da = b, df/db = a
-   * const g = dl.valueAndGrads(f);
+   * const g = tf.valueAndGrads(f);
    *
-   * const a = dl.tensor1d([2, 3]);
-   * const b = dl.tensor1d([-2, -3]);
+   * const a = tf.tensor1d([2, 3]);
+   * const b = tf.tensor1d([-2, -3]);
    * const {value, grads} = g([a, b]);
    *
    * const [da, db] = grads;
@@ -257,14 +257,14 @@ export class Gradients {
    * defaults to all trainable variables.
    *
    * ```js
-   * const a = dl.variable(dl.tensor1d([3, 4]));
-   * const b = dl.variable(dl.tensor1d([5, 6]));
-   * const x = dl.tensor1d([1, 2]);
+   * const a = tf.variable(tf.tensor1d([3, 4]));
+   * const b = tf.variable(tf.tensor1d([5, 6]));
+   * const x = tf.tensor1d([1, 2]);
    *
    * // f(a, b) = a * x ^ 2 + b * x
    * const f = () => a.mul(x.square()).add(b.mul(x)).sum();
    * // df/da = x ^ 2, df/db = x
-   * const {value, grads} = dl.variableGrads(f);
+   * const {value, grads} = tf.variableGrads(f);
    *
    * Object.keys(grads).forEach(varName => grads[varName].print());
    * ```
@@ -332,13 +332,13 @@ export class Gradients {
    * respect to each input of `f` are computed using `f().gradFunc`.
    *
    * ```js
-   * const customOp = dl.customGrad(x => {
+   * const customOp = tf.customGrad(x => {
    *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);
    *   return {value: x.square(), gradFunc: dy => [dy.mul(x.abs())]};
    * });
    *
-   * const x = dl.tensor1d([-1, -2, 3]);
-   * const dx = dl.grad(x => customOp(x));
+   * const x = tf.tensor1d([-1, -2, 3]);
+   * const dx = tf.grad(x => customOp(x));
    *
    * console.log(`f(x):`);
    * customOp(x).print();
diff --git a/src/ops/array_ops.ts b/src/ops/array_ops.ts
index a6a37723df..6ce0130063 100644
--- a/src/ops/array_ops.ts
+++ b/src/ops/array_ops.ts
@@ -36,18 +36,18 @@ export class ArrayOps {
    *
    * ```js
    * // Pass an array of values to create a vector.
-   * dl.tensor([1, 2, 3, 4]).print();
+   * tf.tensor([1, 2, 3, 4]).print();
    * ```
    *
    * ```js
    * // Pass a nested array of values to make a matrix or a higher
    * // dimensional tensor.
-   * dl.tensor([[1, 2], [3, 4]]).print();
+   * tf.tensor([[1, 2], [3, 4]]).print();
    * ```
    *
    * ```js
    * // Pass a flat array and specify a shape yourself.
-   * dl.tensor([1, 2, 3, 4], [2, 2]).print();
+   * tf.tensor([1, 2, 3, 4], [2, 2]).print();
    * ```
    *
    * @param values The values of the tensor. Can be nested array of numbers,
@@ -80,12 +80,11 @@ export class ArrayOps {
   /**
    * Creates rank-0 `Tensor` (scalar) with the provided value and dtype.
    *
-   * This method is mainly for self documentation and TypeScript typings as the
-   * same functionality can be achieved with `tensor`. In general, we recommend
-   * using this method as it makes code more readable.
+   * The same functionality can be achieved with `tensor`, but in general
+   * we recommend using `scalar` as it makes the code more readable.
    *
    * ```js
-   * dl.scalar(3.14).print();
+   * tf.scalar(3.14).print();
    * ```
    *
    * @param value The value of the scalar.
@@ -104,12 +103,11 @@ export class ArrayOps {
   /**
    * Creates rank-1 `Tensor` with the provided values, shape and dtype.
    *
-   * This method is mainly for self documentation and TypeScript typings as the
-   * same functionality can be achieved with `tensor`. In general, we recommend
-   * using this method as it makes code more readable.
+   * The same functionality can be achieved with `tensor`, but in general
+   * we recommend using `tensor1d` as it makes the code more readable.
    *
    * ```js
-   * dl.tensor1d([1, 2, 3]).print();
+   * tf.tensor1d([1, 2, 3]).print();
    * ```
    *
    * @param values The values of the tensor. Can be array of numbers,
@@ -129,17 +127,16 @@ export class ArrayOps {
   /**
    * Creates rank-2 `Tensor` with the provided values, shape and dtype.
    *
-   * This method is mainly for self documentation and TypeScript typings as the
-   * same functionality can be achieved with `tensor`. In general, we recommend
-   * using this method as it makes code more readable.
+   * The same functionality can be achieved with `tensor`, but in general
+   * we recommend using `tensor2d` as it makes the code more readable.
    *
    *  ```js
    * // Pass a nested array.
-   * dl.tensor2d([[1, 2], [3, 4]]).print();
+   * tf.tensor2d([[1, 2], [3, 4]]).print();
    * ```
    * ```js
    * // Pass a flat array and specify a shape.
-   * dl.tensor2d([1, 2, 3, 4], [2, 2]).print();
+   * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();
    * ```
    *
    * @param values The values of the tensor. Can be nested array of numbers,
@@ -165,17 +162,16 @@ export class ArrayOps {
   /**
    * Creates rank-3 `Tensor` with the provided values, shape and dtype.
    *
-   * This method is mainly for self documentation and TypeScript typings as
-   * the same functionality can be achieved with `tensor`. In general, we
-   * recommend using this method as it makes code more readable.
+   * The same functionality can be achieved with `tensor`, but in general
+   * we recommend using `tensor3d` as it makes the code more readable.
    *
    *  ```js
    * // Pass a nested array.
-   * dl.tensor3d([[[1], [2]], [[3], [4]]]).print();
+   * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();
    * ```
    * ```js
    * // Pass a flat array and specify a shape.
-   * dl.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();
+   * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();
    * ```
    *
    * @param values The values of the tensor. Can be nested array of numbers,
@@ -200,13 +196,17 @@ export class ArrayOps {
 
   /**
    * Creates rank-4 `Tensor` with the provided values, shape and dtype.
+   *
+   * The same functionality can be achieved with `tensor`, but in general
+   * we recommend using `tensor4d` as it makes the code more readable.
+   *
    *  ```js
    * // Pass a nested array.
-   * dl.tensor4d([[[[1], [2]], [[3], [4]]]]).print();
+   * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();
    * ```
    * ```js
    * // Pass a flat array and specify a shape.
-   * dl.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();
+   * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();
    * ```
    *
    * @param values The values of the tensor. Can be nested array of numbers,
@@ -233,7 +233,7 @@ export class ArrayOps {
    * Creates a `Tensor` with all elements set to 1.
    *
    * ```js
-   * dl.ones([2, 2]).print();
+   * tf.ones([2, 2]).print();
    * ```
    *
    * @param shape An array of integers defining the output tensor shape.
@@ -252,7 +252,7 @@ export class ArrayOps {
    * Creates a `Tensor` with all elements set to 0.
    *
    * ```js
-   * dl.zeros([2, 2]).print();
+   * tf.zeros([2, 2]).print();
    * ```
    *
    * @param shape An array of integers defining the output tensor shape.
@@ -271,7 +271,7 @@ export class ArrayOps {
    * Creates a `Tensor` filled with a scalar value.
    *
    * ```js
-   * dl.fill([2, 2], 4).print();
+   * tf.fill([2, 2], 4).print();
    * ```
    *
    * @param shape An array of integers defining the output tensor shape.
@@ -295,8 +295,8 @@ export class ArrayOps {
    * given tensor.
    *
    * ```js
-   * const x = dl.tensor([1, 2]);
-   * dl.onesLike(x).print();
+   * const x = tf.tensor([1, 2]);
+   * tf.onesLike(x).print();
    * ```
    * @param x A tensor.
    */
@@ -311,8 +311,8 @@ export class ArrayOps {
    * given tensor.
    *
    * ```js
-   * const x = dl.tensor([1, 2]);
-   * dl.zerosLike(x).print();
+   * const x = tf.tensor([1, 2]);
+   * tf.zerosLike(x).print();
    * ```
    *
    * @param x The tensor of required shape.
@@ -328,7 +328,7 @@ export class ArrayOps {
    * tensor.
    *
    * ```js
-   * const x = dl.tensor([1, 2]);
+   * const x = tf.tensor([1, 2]);
    * x.clone().print();
    * ```
    *
@@ -344,7 +344,7 @@ export class ArrayOps {
    * Creates a `Tensor` with values sampled from a normal distribution.
    *
    * ```js
-   * dl.randomNormal([2, 2]).print();
+   * tf.randomNormal([2, 2]).print();
    * ```
    *
    * @param shape An array of integers defining the output tensor shape.
@@ -375,7 +375,7 @@ export class ArrayOps {
    * distribution.
    *
    * ```js
-   * dl.truncatedNormal([2, 2]).print();
+   * tf.truncatedNormal([2, 2]).print();
    * ```
    *
    * The generated values follow a normal distribution with specified mean and
@@ -413,7 +413,7 @@ export class ArrayOps {
    * bound maxval is excluded.
    *
    * ```js
-   * dl.randomUniform([2, 2]).print();
+   * tf.randomUniform([2, 2]).print();
    * ```
    *
    * @param shape An array of integers defining the output tensor shape.
@@ -518,7 +518,7 @@ export class ArrayOps {
    * `offValue` (defaults to 0).
    *
    * ```js
-   * dl.oneHot(dl.tensor1d([0, 1]), 3).print();
+   * tf.oneHot(tf.tensor1d([0, 1]), 3).print();
    * ```
    *
    * @param indices 1D Array of indices.
@@ -550,7 +550,7 @@ export class ArrayOps {
    * image.data[2] = 200;
    * image.data[3] = 255;
    *
-   * dl.fromPixels(image).print();
+   * tf.fromPixels(image).print();
    * ```
    *
    * @param pixels The input image to construct the tensor from.
@@ -587,7 +587,7 @@ export class ArrayOps {
    * tensor.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3, 4]);
+   * const x = tf.tensor1d([1, 2, 3, 4]);
    * x.reshape([2, 2]).print();
    * ```
    *
@@ -613,7 +613,7 @@ export class ArrayOps {
    * Removes dimensions of size 1 from the shape of a `Tensor`.
    *
    * ```js
-   * const x = dl.tensor([1, 2, 3, 4], [1, 1, 4]);
+   * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);
    * x.squeeze().print();
    * ```
    *
@@ -631,8 +631,8 @@ export class ArrayOps {
    * Casts a `Tensor` to a new dtype.
    *
    * ```js
-   * const x = dl.tensor1d([1.5, 2.5, 3]);
-   * dl.cast(x, 'int32').print();
+   * const x = tf.tensor1d([1.5, 2.5, 3]);
+   * tf.cast(x, 'int32').print();
    * ```
    * @param x The input tensor to be casted.
    * @param dtype The dtype to cast the input tensor to.
@@ -657,13 +657,13 @@ export class ArrayOps {
    * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.
    *
    * ```js
-   * const a = dl.tensor1d([1, 2]);
+   * const a = tf.tensor1d([1, 2]);
    *
    * a.tile([2]).print();    // or a.tile([2])
    * ```
    *
    * ```js
-   * const a = dl.tensor2d([1, 2, 3, 4], [2, 2]);
+   * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
    *
    * a.tile([1, 2]).print();  // or a.tile([1, 2])
    * ```
@@ -734,15 +734,15 @@ export class ArrayOps {
    * Gather slices from tensor `x`'s axis `axis` according to `indices`.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3, 4]);
-   * const indices = dl.tensor1d([1, 3, 3]);
+   * const x = tf.tensor1d([1, 2, 3, 4]);
+   * const indices = tf.tensor1d([1, 3, 3]);
    *
    * x.gather(indices).print();
    * ```
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]);
-   * const indices = dl.tensor1d([1, 1, 0]);
+   * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+   * const indices = tf.tensor1d([1, 1, 0]);
    *
    * x.gather(indices).print();
    * ```
@@ -818,11 +818,10 @@ export class ArrayOps {
   /**
    * Pads a `Tensor` with a given value and paddings.
    *
-   * This operation currently only implements the `CONSTANT` mode from
-   * Tensorflow's `pad` operation.
+   * This operation currently only implements the `CONSTANT` mode.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3, 4]);
+   * const x = tf.tensor1d([1, 2, 3, 4]);
    * x.pad([[1, 2]]).print();
    * ```
    * @param x The tensor to pad.
@@ -853,10 +852,10 @@ export class ArrayOps {
    * Stacks a list of rank-`R` `Tensor`s into one rank-`(R+1)` `Tensor`.
    *
    * ```js
-   * const a = dl.tensor1d([1, 2]);
-   * const b = dl.tensor1d([3, 4]);
-   * const c = dl.tensor1d([5, 6]);
-   * dl.stack([a, b, c]).print();
+   * const a = tf.tensor1d([1, 2]);
+   * const b = tf.tensor1d([3, 4]);
+   * const c = tf.tensor1d([5, 6]);
+   * tf.stack([a, b, c]).print();
    * ```
    *
    * @param tensors A list of tensor objects with the same shape and dtype.
@@ -892,7 +891,7 @@ export class ArrayOps {
    * into the tensor's shape.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3, 4]);
+   * const x = tf.tensor1d([1, 2, 3, 4]);
    * const axis = 1;
    * x.expandDims(axis).print();
    * ```
@@ -914,7 +913,7 @@ export class ArrayOps {
    * Return an evenly spaced sequence of numbers over the given interval.
    *
    * ```js
-   * dl.linspace(0, 9, 10).print();
+   * tf.linspace(0, 9, 10).print();
    * ```
    * @param start The start value of the sequence.
    * @param stop The end value of the sequence.
@@ -948,7 +947,7 @@ export class ArrayOps {
    * supported.
    *
    * ```js
-   * dl.range(0, 9, 2).print();
+   * tf.range(0, 9, 2).print();
    * ```
    *
    * @param start An integer start value
@@ -1003,7 +1002,7 @@ export class ArrayOps {
    *
    * ```js
    * // Create a buffer and set values at particular indices.
-   * const buffer = dl.buffer([2, 2]);
+   * const buffer = tf.buffer([2, 2]);
    * buffer.set(3, 0, 0);
    * buffer.set(5, 1, 0);
    *
@@ -1027,7 +1026,7 @@ export class ArrayOps {
    *
    * ```js
    * const verbose = true;
-   * dl.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);
+   * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);
    * ```
    * @param x The tensor to be printed.
    * @param verbose Whether to print verbose information about the ` Tensor`,
diff --git a/src/ops/binary_ops.ts b/src/ops/binary_ops.ts
index 2a86523fc0..672f8aec32 100644
--- a/src/ops/binary_ops.ts
+++ b/src/ops/binary_ops.ts
@@ -32,18 +32,18 @@ export class BinaryOps {
    * asserts that `a` and `b` are the same shape (does not broadcast).
    *
    * ```js
-   * const a = dl.tensor1d([1, 2, 3, 4]);
-   * const b = dl.tensor1d([10, 20, 30, 40]);
+   * const a = tf.tensor1d([1, 2, 3, 4]);
+   * const b = tf.tensor1d([10, 20, 30, 40]);
    *
-   * a.add(b).print();  // or dl.add(a, b)
+   * a.add(b).print();  // or tf.add(a, b)
    * ```
    *
    * ```js
    * // Broadcast add a with b.
-   * const a = dl.scalar(5);
-   * const b = dl.tensor1d([10, 20, 30, 40]);
+   * const a = tf.scalar(5);
+   * const b = tf.tensor1d([10, 20, 30, 40]);
    *
-   * a.add(b).print();  // or dl.add(a, b)
+   * a.add(b).print();  // or tf.add(a, b)
    * ```
    * @param a The first `Tensor` to add.
    * @param b The second `Tensor` to add. Must have the same type as `a`.
@@ -98,18 +98,18 @@ export class BinaryOps {
    * asserts that `a` and `b` are the same shape (does not broadcast).
    *
    * ```js
-   * const a = dl.tensor1d([10, 20, 30, 40]);
-   * const b = dl.tensor1d([1, 2, 3, 4]);
+   * const a = tf.tensor1d([10, 20, 30, 40]);
+   * const b = tf.tensor1d([1, 2, 3, 4]);
    *
-   * a.sub(b).print();  // or dl.sub(a, b)
+   * a.sub(b).print();  // or tf.sub(a, b)
    * ```
    *
    * ```js
    * // Broadcast subtract a with b.
-   * const a = dl.tensor1d([10, 20, 30, 40]);
-   * const b = dl.scalar(5);
+   * const a = tf.tensor1d([10, 20, 30, 40]);
+   * const b = tf.scalar(5);
    *
-   * a.sub(b).print();  // or dl.sub(a, b)
+   * a.sub(b).print();  // or tf.sub(a, b)
    * ```
    * @param a The first `Tensor` to subtract from.
    * @param b The second `Tensor` to be subtracted. Must have the same dtype as
@@ -167,17 +167,17 @@ export class BinaryOps {
    * corresponding elements in x and y.
    *
    * ```js
-   * const a = dl.tensor([[2, 3], [4, 5]])
-   * const b = dl.tensor([[1, 2], [3, 0]]).toInt();
+   * const a = tf.tensor([[2, 3], [4, 5]])
+   * const b = tf.tensor([[1, 2], [3, 0]]).toInt();
    *
-   * a.pow(b).print();  // or dl.pow(a, b)
+   * a.pow(b).print();  // or tf.pow(a, b)
    * ```
    *
    * ```js
-   * const a = dl.tensor([[1, 2], [3, 4]])
-   * const b = dl.tensor(2).toInt();
+   * const a = tf.tensor([[1, 2], [3, 4]])
+   * const b = tf.tensor(2).toInt();
    *
-   * a.pow(b).print();  // or dl.pow(a, b)
+   * a.pow(b).print();  // or tf.pow(a, b)
    * ```
    * We also expose `powStrict` which has the same signature as this op and
    * asserts that `base` and `exp` are the same shape (does not broadcast).
@@ -198,8 +198,8 @@ export class BinaryOps {
       }
       const derBase = () => {
         const expFloat = exp.toFloat();
-        const dx = expFloat.mul(
-                       base.toFloat().pow(expFloat.sub(scalar(1)))) as T;
+        const dx =
+            expFloat.mul(base.toFloat().pow(expFloat.sub(scalar(1)))) as T;
         return dy.mulStrict(dx) as T;
       };
       return {base: derBase};
@@ -230,18 +230,18 @@ export class BinaryOps {
    * asserts that `a` and `b` are the same shape (does not broadcast).
    *
    * ```js
-   * const a = dl.tensor1d([1, 2, 3, 4]);
-   * const b = dl.tensor1d([2, 3, 4, 5]);
+   * const a = tf.tensor1d([1, 2, 3, 4]);
+   * const b = tf.tensor1d([2, 3, 4, 5]);
    *
-   * a.mul(b).print();  // or dl.mul(a, b)
+   * a.mul(b).print();  // or tf.mul(a, b)
    * ```
    *
    * ```js
    * // Broadcast mul a with b.
-   * const a = dl.tensor1d([1, 2, 3, 4]);
-   * const b = dl.scalar(5);
+   * const a = tf.tensor1d([1, 2, 3, 4]);
+   * const b = tf.scalar(5);
    *
-   * a.mul(b).print();  // or dl.mul(a, b)
+   * a.mul(b).print();  // or tf.mul(a, b)
    * ```
    * @param a The first tensor to multiply.
    * @param b The second tensor to multiply. Must have the same dtype as `a`.
@@ -298,18 +298,18 @@ export class BinaryOps {
    * asserts that `a` and `b` are the same shape (does not broadcast).
    *
    * ```js
-   * const a = dl.tensor1d([1, 4, 9, 16]);
-   * const b = dl.tensor1d([1, 2, 3, 4]);
+   * const a = tf.tensor1d([1, 4, 9, 16]);
+   * const b = tf.tensor1d([1, 2, 3, 4]);
    *
-   * a.div(b).print();  // or dl.div(a, b)
+   * a.div(b).print();  // or tf.div(a, b)
    * ```
    *
    * ```js
    * // Broadcast div a with b.
-   * const a = dl.tensor1d([2, 4, 6, 8]);
-   * const b = dl.scalar(2);
+   * const a = tf.tensor1d([2, 4, 6, 8]);
+   * const b = tf.scalar(2);
    *
-   * a.div(b).print();  // or dl.div(a, b)
+   * a.div(b).print();  // or tf.div(a, b)
    * ```
    *
    * @param a The first tensor as the numerator.
@@ -366,18 +366,18 @@ export class BinaryOps {
    * asserts that `a` and `b` are the same shape (does not broadcast).
    *
    * ```js
-   * const a = dl.tensor1d([1, 4, 3, 16]);
-   * const b = dl.tensor1d([1, 2, 9, 4]);
+   * const a = tf.tensor1d([1, 4, 3, 16]);
+   * const b = tf.tensor1d([1, 2, 9, 4]);
    *
-   * a.minimum(b).print();  // or dl.minimum(a, b)
+   * a.minimum(b).print();  // or tf.minimum(a, b)
    * ```
    *
    * ```js
    * // Broadcast minimum a with b.
-   * const a = dl.tensor1d([2, 4, 6, 8]);
-   * const b = dl.scalar(5);
+   * const a = tf.tensor1d([2, 4, 6, 8]);
+   * const b = tf.scalar(5);
    *
-   * a.minimum(b).print();  // or dl.minimum(a, b)
+   * a.minimum(b).print();  // or tf.minimum(a, b)
    * ```
    *
    * @param a The first tensor.
@@ -418,18 +418,18 @@ export class BinaryOps {
    * asserts that `a` and `b` are the same shape (does not broadcast).
    *
    * ```js
-   * const a = dl.tensor1d([1, 4, 3, 16]);
-   * const b = dl.tensor1d([1, 2, 9, 4]);
+   * const a = tf.tensor1d([1, 4, 3, 16]);
+   * const b = tf.tensor1d([1, 2, 9, 4]);
    *
-   * a.maximum(b).print();  // or dl.maximum(a, b)
+   * a.maximum(b).print();  // or tf.maximum(a, b)
    * ```
    *
    * ```js
    * // Broadcast maximum a with b.
-   * const a = dl.tensor1d([2, 4, 6, 8]);
-   * const b = dl.scalar(5);
+   * const a = tf.tensor1d([2, 4, 6, 8]);
+   * const b = tf.scalar(5);
    *
-   * a.maximum(b).print();  // or dl.maximum(a, b)
+   * a.maximum(b).print();  // or tf.maximum(a, b)
    * ```
    *
    * @param a The first tensor.
diff --git a/src/ops/concat.ts b/src/ops/concat.ts
index e5e805808a..e5af04babc 100644
--- a/src/ops/concat.ts
+++ b/src/ops/concat.ts
@@ -122,23 +122,23 @@ export class ConcatOps {
    * dimensions except `axis`.
    *
    * ```js
-   * const a = dl.tensor1d([1, 2]);
-   * const b = dl.tensor1d([3, 4]);
+   * const a = tf.tensor1d([1, 2]);
+   * const b = tf.tensor1d([3, 4]);
    * a.concat(b).print();  // or a.concat(b)
    * ```
    *
    * ```js
-   * const a = dl.tensor1d([1, 2]);
-   * const b = dl.tensor1d([3, 4]);
-   * const c = dl.tensor1d([5, 6]);
-   * dl.concat([a, b, c]).print();
+   * const a = tf.tensor1d([1, 2]);
+   * const b = tf.tensor1d([3, 4]);
+   * const c = tf.tensor1d([5, 6]);
+   * tf.concat([a, b, c]).print();
    * ```
    *
    * ```js
-   * const a = dl.tensor2d([[1, 2], [10, 20]]);
-   * const b = dl.tensor2d([[3, 4], [30, 40]]);
+   * const a = tf.tensor2d([[1, 2], [10, 20]]);
+   * const b = tf.tensor2d([[3, 4], [30, 40]]);
    * const axis = 1;
-   * dl.concat([a, b], axis).print();
+   * tf.concat([a, b], axis).print();
    * ```
    * @param tensors A list of tensors to concatenate.
    * @param axis The axis to concate along. Defaults to 0 (the first dim).
diff --git a/src/ops/matmul.ts b/src/ops/matmul.ts
index 92f2704ebc..b41cf56574 100644
--- a/src/ops/matmul.ts
+++ b/src/ops/matmul.ts
@@ -26,10 +26,10 @@ export class MatmulOps {
    * Computes the dot product of two matrices, A * B. These must be matrices.
    *
    * ```js
-   * const a = dl.tensor2d([1, 2], [1, 2]);
-   * const b = dl.tensor2d([1, 2, 3, 4], [2, 2]);
+   * const a = tf.tensor2d([1, 2], [1, 2]);
+   * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
    *
-   * a.matMul(b).print();  // or dl.matMul(a, b)
+   * a.matMul(b).print();  // or tf.matMul(a, b)
    * ```
    * @param a First matrix in dot product operation.
    * @param b Second matrix in dot product operation.
@@ -140,10 +140,10 @@ export class MatmulOps {
    * Computes the outer product of two vectors, v1 and v2.
    *
    * ```js
-   * const a = dl.tensor1d([1, 2, 3]);
-   * const b = dl.tensor1d([3, 4, 5]);
+   * const a = tf.tensor1d([1, 2, 3]);
+   * const b = tf.tensor1d([3, 4, 5]);
    *
-   * dl.outerProduct(a, b).print();
+   * tf.outerProduct(a, b).print();
    * ```
    * @param v1 The first vector in the outer product operation.
    * @param v2 The second vector in the dot product operation.
diff --git a/src/ops/norm.ts b/src/ops/norm.ts
index 50685266b1..557f03e9f3 100644
--- a/src/ops/norm.ts
+++ b/src/ops/norm.ts
@@ -30,9 +30,9 @@ export class NormOps {
    * and matrix norms (Frobenius, 1-norm, and inf-norm).
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3, 4]);
+   * const x = tf.tensor1d([1, 2, 3, 4]);
    *
-   * x.norm().print();  // or dl.norm(x)
+   * x.norm().print();  // or tf.norm(x)
    * ```
    *
    * @param x The input array.
diff --git a/src/ops/reduction_ops.ts b/src/ops/reduction_ops.ts
index 561b36061f..cd4a5a3b9d 100644
--- a/src/ops/reduction_ops.ts
+++ b/src/ops/reduction_ops.ts
@@ -35,16 +35,16 @@ export class ReductionOps {
    * single element is returned.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3]);
+   * const x = tf.tensor1d([1, 2, 3]);
    *
-   * x.logSumExp().print();  // or dl.logSumExp(x)
+   * x.logSumExp().print();  // or tf.logSumExp(x)
    * ```
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]);
+   * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
    *
    * const axis = 1;
-   * x.logSumExp(axis).print();  // or dl.logSumExp(a, axis)
+   * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)
    * ```
    * @param input The input tensor.
    * @param axis The dimension(s) to reduce. If null (the default),
@@ -81,16 +81,16 @@ export class ReductionOps {
    * single element is returned.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3]);
+   * const x = tf.tensor1d([1, 2, 3]);
    *
-   * x.sum().print();  // or dl.logSumExp(x)
+   * x.sum().print();  // or tf.logSumExp(x)
    * ```
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]);
+   * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
    *
    * const axis = 1;
-   * x.sum(axis).print();  // or dl.sum(x, axis)
+   * x.sum(axis).print();  // or tf.sum(x, axis)
    * ```
    *
    * @param x The input tensor to compute the sum over.
@@ -147,16 +147,16 @@ export class ReductionOps {
    * a single element is returned.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3]);
+   * const x = tf.tensor1d([1, 2, 3]);
    *
-   * x.mean().print();  // or dl.logSumExp(a)
+   * x.mean().print();  // or tf.logSumExp(a)
    * ```
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]);
+   * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
    *
    * const axis = 1;
-   * x.mean(axis).print();  // or dl.mean(x, axis)
+   * x.mean(axis).print();  // or tf.mean(x, axis)
    * ```
    *
    * @param x The input tensor.
@@ -206,16 +206,16 @@ export class ReductionOps {
    * single element is returned.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3]);
+   * const x = tf.tensor1d([1, 2, 3]);
    *
-   * x.min().print();  // or dl.min(x)
+   * x.min().print();  // or tf.min(x)
    * ```
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]);
+   * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
    *
    * const axis = 1;
-   * x.min(axis).print();  // or dl.min(x, axis)
+   * x.min(axis).print();  // or tf.min(x, axis)
    * ```
    *
    * @param x The input Tensor.
@@ -252,16 +252,16 @@ export class ReductionOps {
    * a single element is returned.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3]);
+   * const x = tf.tensor1d([1, 2, 3]);
    *
-   * x.max().print();  // or dl.max(x)
+   * x.max().print();  // or tf.max(x)
    * ```
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]);
+   * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
    *
    * const axis = 1;
-   * x.max(axis).print();  // or dl.max(x, axis)
+   * x.max(axis).print();  // or tf.max(x, axis)
    * ```
    *
    * @param x The input tensor.
@@ -295,16 +295,16 @@ export class ReductionOps {
    * removed.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3]);
+   * const x = tf.tensor1d([1, 2, 3]);
    *
-   * x.argMin().print();  // or dl.argMin(x)
+   * x.argMin().print();  // or tf.argMin(x)
    * ```
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, 4, 3], [2, 2]);
+   * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
    *
    * const axis = 1;
-   * x.argMin(axis).print();  // or dl.argMin(x, axis)
+   * x.argMin(axis).print();  // or tf.argMin(x, axis)
    * ```
    *
    * @param x The input tensor.
@@ -331,16 +331,16 @@ export class ReductionOps {
    * removed.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3]);
+   * const x = tf.tensor1d([1, 2, 3]);
    *
-   * x.argMax().print();  // or dl.argMax(x)
+   * x.argMax().print();  // or tf.argMax(x)
    * ```
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, 4, 3], [2, 2]);
+   * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
    *
    * const axis = 1;
-   * x.argMax(axis).print();  // or dl.argMax(x, axis)
+   * x.argMax(axis).print();  // or tf.argMax(x, axis)
    * ```
    *
    * @param x The input tensor.
diff --git a/src/ops/reverse.ts b/src/ops/reverse.ts
index 20bd76b4c0..ee413813da 100644
--- a/src/ops/reverse.ts
+++ b/src/ops/reverse.ts
@@ -75,13 +75,13 @@ export class ReverseOps {
    * Reverses a `Tensor` along a specified axis.
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3, 4]);
+   * const x = tf.tensor1d([1, 2, 3, 4]);
    *
    * x.reverse().print();
    * ```
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]);
+   * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
    *
    * const axis = 1;
    * x.reverse(axis).print();
diff --git a/src/ops/slice.ts b/src/ops/slice.ts
index 4437a0d4a1..7e942000b4 100644
--- a/src/ops/slice.ts
+++ b/src/ops/slice.ts
@@ -79,19 +79,19 @@ export class SliceOps {
    *
    * Also available are stricter rank-specific methods with the same signature
    * as this method that assert that `x` is of the given rank:
-   *   - `dl.slice1d`
-   *   - `dl.slice2d`
-   *   - `dl.slice3d`
-   *   - `dl.slice4d`
+   *   - `tf.slice1d`
+   *   - `tf.slice2d`
+   *   - `tf.slice3d`
+   *   - `tf.slice4d`
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 3, 4]);
+   * const x = tf.tensor1d([1, 2, 3, 4]);
    *
    * x.slice([1], [2]).print();
    * ```
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]);
+   * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
    *
    * x.slice([1, 0], [1, 2]).print();
    * ```
diff --git a/src/ops/softmax.ts b/src/ops/softmax.ts
index ea1cc84cd8..cd4c0b977a 100644
--- a/src/ops/softmax.ts
+++ b/src/ops/softmax.ts
@@ -29,15 +29,15 @@ export class SoftmaxOps {
    * Computes the softmax normalized vector given the logits.
    *
    * ```js
-   * const a = dl.tensor1d([1, 2, 3]);
+   * const a = tf.tensor1d([1, 2, 3]);
    *
-   * a.softmax().print();  // or dl.softmax(a)
+   * a.softmax().print();  // or tf.softmax(a)
    * ```
    *
    * ```js
-   * const a = dl.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
+   * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
    *
-   * a.softmax().print();  // or dl.softmax(a)
+   * a.softmax().print();  // or tf.softmax(a)
    * ```
    *
    * @param logits The logits array.
diff --git a/src/ops/transpose.ts b/src/ops/transpose.ts
index a725174075..d2a18f99ad 100644
--- a/src/ops/transpose.ts
+++ b/src/ops/transpose.ts
@@ -32,9 +32,9 @@ export class TransposeOps {
    * operation performs a regular matrix transpose on 2-D input `Tensor`s.
    *
    * ```js
-   * const a = dl.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+   * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
    *
-   * a.transpose().print();  // or dl.transpose(a)
+   * a.transpose().print();  // or tf.transpose(a)
    * ```
    *
    * @param x The tensor to transpose.
diff --git a/src/ops/unary_ops.ts b/src/ops/unary_ops.ts
index 01ef0d370e..aed5d3efb2 100644
--- a/src/ops/unary_ops.ts
+++ b/src/ops/unary_ops.ts
@@ -29,9 +29,9 @@ export class UnaryOps {
    * Computes `-1 * x` element-wise.
    *
    * ```js
-   * const x = dl.tensor2d([1, 2, -2, 0], [2, 2]);
+   * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);
    *
-   * x.neg().print();  // or dl.neg(x)
+   * x.neg().print();  // or tf.neg(x)
    * ```
    *
    * @param x The input tensor.
@@ -49,9 +49,9 @@ export class UnaryOps {
    * Computes ceiling of input `Tensor` element-wise: `ceil(x)`
    *
    * ```js
-   * const x = dl.tensor1d([.6, 1.1, -3.3]);
+   * const x = tf.tensor1d([.6, 1.1, -3.3]);
    *
-   * x.ceil().print();  // or dl.ceil(x)
+   * x.ceil().print();  // or tf.ceil(x)
    * ```
    * @param x The input Tensor.
    */
@@ -69,9 +69,9 @@ export class UnaryOps {
    * Computes floor of input `Tensor` element-wise: `floor(x)`.
    *
    * ```js
-   * const x = dl.tensor1d([.6, 1.1, -3.3]);
+   * const x = tf.tensor1d([.6, 1.1, -3.3]);
    *
-   * x.floor().print();  // or dl.floor(x)
+   * x.floor().print();  // or tf.floor(x)
    * ```
    * @param x The input tensor.
    */
@@ -90,9 +90,9 @@ export class UnaryOps {
    * Computes exponential of the input `Tensor` element-wise. `e ^ x`
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, -3]);
+   * const x = tf.tensor1d([1, 2, -3]);
    *
-   * x.exp().print();  // or dl.exp(x)
+   * x.exp().print();  // or tf.exp(x)
    * ```
    * @param x The input tensor.
    */
@@ -111,9 +111,9 @@ export class UnaryOps {
    * Computes natural logarithm of the input `Tensor` element-wise: `ln(x)`
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, Math.E]);
+   * const x = tf.tensor1d([1, 2, Math.E]);
    *
-   * x.log().print();  // or dl.log(x)
+   * x.log().print();  // or tf.log(x)
    * ```
    * @param x The input tensor.
    */
@@ -131,9 +131,9 @@ export class UnaryOps {
    * element-wise: `ln(1 + x)`
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, Math.E - 1]);
+   * const x = tf.tensor1d([1, 2, Math.E - 1]);
    *
-   * x.log1p().print();  // or dl.log1p(x)
+   * x.log1p().print();  // or tf.log1p(x)
    * ```
    * @param x The input tensor.
    */
@@ -150,9 +150,9 @@ export class UnaryOps {
    * Computes square root of the input `Tensor` element-wise: `y = sqrt(x)`
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, 4, -1]);
+   * const x = tf.tensor1d([1, 2, 4, -1]);
    *
-   * x.sqrt().print();  // or dl.sqrt(x)
+   * x.sqrt().print();  // or tf.sqrt(x)
    * ```
    * @param x The input tensor.
    */
@@ -169,9 +169,9 @@ export class UnaryOps {
    * Computes square of `x` element-wise: `x ^ 2`
    *
    * ```js
-   * const x = dl.tensor1d([1, 2, Math.sqrt(2), -1]);
+   * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);
    *
-   * x.square().print();  // or dl.square(x)
+   * x.square().print();  // or tf.square(x)
    * ```
    * @param x The input Tensor.
    */
@@ -188,9 +188,9 @@ export class UnaryOps {
    * Computes absolute value element-wise: `abs(x)`
    *
    * ```js
-   * const x = dl.tensor1d([-1, 2, -3, 4]);
+   * const x = tf.tensor1d([-1, 2, -3, 4]);
    *
-   * x.abs().print();  // or dl.abs(x)
+   * x.abs().print();  // or tf.abs(x)
    * ```
    * @param x The input `Tensor`.
    */
@@ -207,9 +207,9 @@ export class UnaryOps {
    * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`
    *
    * ```js
-   * const x = dl.tensor1d([-1, 2, -3, 4]);
+   * const x = tf.tensor1d([-1, 2, -3, 4]);
    *
-   * x.clipByValue(-2, 3).print();  // or dl.clipByValue(x, -2, 3)
+   * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)
    * ```
    * @param x The input tensor.
    * @param clipValueMin Lower-bound of range to be clipped to.
@@ -241,9 +241,9 @@ export class UnaryOps {
    * Computes rectified linear element-wise: `max(x, 0)`
    *
    * ```js
-   * const x = dl.tensor1d([-1, 2, -3, 4]);
+   * const x = tf.tensor1d([-1, 2, -3, 4]);
    *
-   * x.relu().print();  // or dl.relu(x)
+   * x.relu().print();  // or tf.relu(x)
    * ```
    * @param x The input tensor.
    */
@@ -261,9 +261,9 @@ export class UnaryOps {
    * Computes exponential linear element-wise, `x > 0 ? e ^ x - 1 : 0`
    *
    * ```js
-   * const x = dl.tensor1d([-1, 1, -3, 2]);
+   * const x = tf.tensor1d([-1, 1, -3, 2]);
    *
-   * x.elu().print();  // or dl.elu(x)
+   * x.elu().print();  // or tf.elu(x)
    * ```
    * @param x The input tensor.
    */
@@ -282,9 +282,9 @@ export class UnaryOps {
    * `x < 0 ? scale * alpha * (exp(x) - 1) : x`
    *
    * ```js
-   * const x = dl.tensor1d([-1, 2, -3, 4]);
+   * const x = tf.tensor1d([-1, 2, -3, 4]);
    *
-   * x.selu().print();  // or dl.selu(x)
+   * x.selu().print();  // or tf.selu(x)
    * ```
    * @param x The input tensor.
    */
@@ -317,9 +317,9 @@ export class UnaryOps {
    *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)
    *
    * ```js
-   * const x = dl.tensor1d([-1, 2, -3, 4]);
+   * const x = tf.tensor1d([-1, 2, -3, 4]);
    *
-   * x.leakyRelu(0.1).print();  // or dl.leakyRelu(x, 0.1)
+   * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)
    * ```
    * @param x The input tensor.
    * @param alpha The scaling factor for negative values, defaults to 0.2.
@@ -340,10 +340,10 @@ export class UnaryOps {
    * `x < 0 ? alpha * x : f(x) = x`
    *
    * ```js
-   * const x = dl.tensor1d([-1, 2, -3, 4]);
-   * const alpha = dl.scalar(0.1);
+   * const x = tf.tensor1d([-1, 2, -3, 4]);
+   * const alpha = tf.scalar(0.1);
    *
-   * x.prelu(alpha).print();  // or dl.prelu(x, alpha)
+   * x.prelu(alpha).print();  // or tf.prelu(x, alpha)
    * ```
    * @param x The input tensor.
    * @param alpha Scaling factor for negative values.
@@ -361,9 +361,9 @@ export class UnaryOps {
    * Computes sigmoid element-wise, `1 / (1 + exp(-x))`
    *
    * ```js
-   * const x = dl.tensor1d([0, -1, 2, -3]);
+   * const x = tf.tensor1d([0, -1, 2, -3]);
    *
-   * x.sigmoid().print();  // or dl.sigmoid(x)
+   * x.sigmoid().print();  // or tf.sigmoid(x)
    * ```
    * @param x The input tensor.
    */
@@ -382,9 +382,9 @@ export class UnaryOps {
    * Computes sin of the input Tensor element-wise: `sin(x)`
    *
    * ```js
-   * const x = dl.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
+   * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
    *
-   * x.sin().print();  // or dl.sin(x)
+   * x.sin().print();  // or tf.sin(x)
    * ```
    * @param x The input tensor.
    */
@@ -401,9 +401,9 @@ export class UnaryOps {
    * Computes cos of the input `Tensor` element-wise: `cos(x)`
    *
    * ```js
-   * const x = dl.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
+   * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
    *
-   * x.cos().print();  // or dl.cos(x)
+   * x.cos().print();  // or tf.cos(x)
    * ```
    * @param x The input tensor.
    */
@@ -420,9 +420,9 @@ export class UnaryOps {
    * Computes tan of the input `Tensor` element-wise, `tan(x)`
    *
    * ```js
-   * const x = dl.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
+   * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
    *
-   * x.tan().print();  // or dl.tan(x)
+   * x.tan().print();  // or tf.tan(x)
    * ```
    * @param x The input tensor.
    */
@@ -439,9 +439,9 @@ export class UnaryOps {
    * Computes asin of the input `Tensor` element-wise: `asin(x)`
    *
    * ```js
-   * const x = dl.tensor1d([0, 1, -1, .7]);
+   * const x = tf.tensor1d([0, 1, -1, .7]);
    *
-   * x.asin().print();  // or dl.asin(x)
+   * x.asin().print();  // or tf.asin(x)
    * ```
    * @param x The input tensor.
    */
@@ -461,9 +461,9 @@ export class UnaryOps {
    * Computes acos of the input `Tensor` element-wise: `acos(x)`
    *
    * ```js
-   * const x = dl.tensor1d([0, 1, -1, .7]);
+   * const x = tf.tensor1d([0, 1, -1, .7]);
    *
-   * x.acos().print();  // or dl.acos(x)
+   * x.acos().print();  // or tf.acos(x)
    * ```
    * @param x The input tensor.
    */
@@ -484,9 +484,9 @@ export class UnaryOps {
    * Computes atan of the input `Tensor` element-wise: `atan(x)`
    *
    * ```js
-   * const x = dl.tensor1d([0, 1, -1, .7]);
+   * const x = tf.tensor1d([0, 1, -1, .7]);
    *
-   * x.atan().print();  // or dl.atan(x)
+   * x.atan().print();  // or tf.atan(x)
    * ```
    * @param x The input tensor.
    */
@@ -503,9 +503,9 @@ export class UnaryOps {
    * Computes hyperbolic sin of the input `Tensor` element-wise: `sinh(x)`
    *
    * ```js
-   * const x = dl.tensor1d([0, 1, -1, .7]);
+   * const x = tf.tensor1d([0, 1, -1, .7]);
    *
-   * x.sinh().print();  // or dl.sinh(x)
+   * x.sinh().print();  // or tf.sinh(x)
    * ```
    * @param x The input tensor.
    */
@@ -522,9 +522,9 @@ export class UnaryOps {
    * Computes hyperbolic cos of the input `Tensor` element-wise: `cosh(x)`
    *
    * ```js
-   * const x = dl.tensor1d([0, 1, -1, .7]);
+   * const x = tf.tensor1d([0, 1, -1, .7]);
    *
-   * x.cosh().print();  // or dl.cosh(x)
+   * x.cosh().print();  // or tf.cosh(x)
    * ```
    * @param x The input tensor.
    */
@@ -541,9 +541,9 @@ export class UnaryOps {
    * Computes hyperbolic tangent of the input `Tensor` element-wise: `tanh(x)`
    *
    * ```js
-   * const x = dl.tensor1d([0, 1, -1, 70]);
+   * const x = tf.tensor1d([0, 1, -1, 70]);
    *
-   * x.tanh().print();  // or dl.tanh(x)
+   * x.tanh().print();  // or tf.tanh(x)
    * ```
    * @param x The input tensor.
    */
@@ -562,9 +562,9 @@ export class UnaryOps {
    * Computes step of the input `Tensor` element-wise: `x > 0 ? 1 : alpha * x`
    *
    * ```js
-   * const x = dl.tensor1d([0, 2, -1, -3]);
+   * const x = tf.tensor1d([0, 2, -1, -3]);
    *
-   * x.step(.5).print();  // or dl.step(x, .5)
+   * x.step(.5).print();  // or tf.step(x, .5)
    * ```
    * @param x The input tensor.
    * @param alpha The gradient when input is negative.
diff --git a/src/optimizers/optimizer_constructors.ts b/src/optimizers/optimizer_constructors.ts
index a77de1068a..9118f11ea1 100644
--- a/src/optimizers/optimizer_constructors.ts
+++ b/src/optimizers/optimizer_constructors.ts
@@ -31,19 +31,19 @@ export class OptimizerConstructors {
    *
    * ```js
    * // Fit a quadratic function by learning the coefficients a, b, c.
-   * const xs = dl.tensor1d([0, 1, 2, 3]);
-   * const ys = dl.tensor1d([1.1, 5.9, 16.8, 33.9]);
+   * const xs = tf.tensor1d([0, 1, 2, 3]);
+   * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);
    *
-   * const a = dl.scalar(Math.random()).variable();
-   * const b = dl.scalar(Math.random()).variable();
-   * const c = dl.scalar(Math.random()).variable();
+   * const a = tf.scalar(Math.random()).variable();
+   * const b = tf.scalar(Math.random()).variable();
+   * const c = tf.scalar(Math.random()).variable();
    *
    * // y = a * x^2 + b * x + c.
    * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);
    * const loss = (pred, label) => pred.sub(label).square().mean();
    *
    * const learningRate = 0.01;
-   * const optimizer = dl.train.sgd(learningRate);
+   * const optimizer = tf.train.sgd(learningRate);
    *
    * // Train the model.
    * for (let i = 0; i < 10; i++) {
diff --git a/src/tensor.ts b/src/tensor.ts
index 08e54444a7..c445893c45 100644
--- a/src/tensor.ts
+++ b/src/tensor.ts
@@ -885,8 +885,8 @@ export class Variable<R extends Rank = Rank> extends Tensor<R> {
   /**
    * Creates a new variable with the provided initial value.
    * ```js
-   * const x = dl.variable(dl.tensor([1, 2, 3]));
-   * x.assign(dl.tensor([4, 5, 6]));
+   * const x = tf.variable(tf.tensor([1, 2, 3]));
+   * x.assign(tf.tensor([4, 5, 6]));
    *
    * x.print();
    * ```
diff --git a/src/tracking.ts b/src/tracking.ts
index b53c912438..639b919b13 100644
--- a/src/tracking.ts
+++ b/src/tracking.ts
@@ -39,20 +39,20 @@ export class Tracking {
    *
    * ```js
    * // y = 2 ^ 2 + 1
-   * const y = dl.tidy(() => {
+   * const y = tf.tidy(() => {
    *   // a, b, and one will be cleaned up when the tidy ends.
-   *   const one = dl.scalar(1);
-   *   const a = dl.scalar(2);
+   *   const one = tf.scalar(1);
+   *   const a = tf.scalar(2);
    *   const b = a.square();
    *
-   *   console.log('numTensors (in tidy): ' + dl.memory().numTensors);
+   *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
    *
    *   // The value returned inside the tidy function will return
    *   // through the tidy, in this case to the variable y.
    *   return b.add(one);
    * });
    *
-   * console.log('numTensors (outside tidy): ' + dl.memory().numTensors);
+   * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
    * y.print();
    * ```
    *
@@ -70,7 +70,7 @@ export class Tracking {
     if (fn == null) {
       // Called with only 1 argument.
       if (typeof nameOrFn !== 'function') {
-        throw new Error('Please provide a function to dl.tidy()');
+        throw new Error('Please provide a function to tidy()');
       }
       fn = nameOrFn;
     } else {
@@ -78,12 +78,12 @@ export class Tracking {
       if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {
         throw new Error(
             'When calling with two arguments, the first argument ' +
-            'to dl.tidy() must be a string');
+            'to tidy() must be a string');
       }
       if (typeof fn !== 'function') {
         throw new Error(
             'When calling with two arguments, the 2nd argument ' +
-            'to dl.tidy() must be a function');
+            'to tidy() must be a function');
       }
       name = nameOrFn as string;
       // TODO(nsthorat,smilkov): Do operation logging and performance
@@ -119,22 +119,22 @@ export class Tracking {
    *
    * ```js
    * let b;
-   * const y = dl.tidy(() => {
-   *   const one = dl.scalar(1);
-   *   const a = dl.scalar(2);
+   * const y = tf.tidy(() => {
+   *   const one = tf.scalar(1);
+   *   const a = tf.scalar(2);
    *
    *   // b will not be cleaned up by the tidy. a and one will be cleaned up
    *   // when the tidy ends.
-   *   b = dl.keep(a.square());
+   *   b = tf.keep(a.square());
    *
-   *   console.log('numTensors (in tidy): ' + dl.memory().numTensors);
+   *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
    *
    *   // The value returned inside the tidy function will return
    *   // through the tidy, in this case to the variable y.
    *   return b.add(one);
    * });
    *
-   * console.log('numTensors (outside tidy): ' + dl.memory().numTensors);
+   * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
    * console.log('y:');
    * y.print();
    * console.log('b:');
@@ -161,8 +161,8 @@ export class Tracking {
    *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).
    *
    * ```js
-   * const x = dl.randomNormal([20, 20]);
-   * const time = await dl.time(() => x.matMul(x));
+   * const x = tf.randomNormal([20, 20]);
+   * const time = await tf.time(() => x.matMul(x));
    *
    * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);
    * ```