Netlify Integration
July 30, 2017
Netlify Netlify makes deploying static sites easy and fast. All you need to do is link a repository to your Netifly account, and every time you git push, Netlift will pull the site to their server, run your build script and deploy the rendered static site on their CDN. It’s free to start, and scales nicely.
This theme intends to make Netifly integration easy and fast. After downloading the theme into your Hugo project, you should be able to just edit the netifly. ...
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Code
July 30, 2017
Pretty looking and readable code using the Roboto Mono font from google. Code doesn’t render nicely in Hugo’s summaries, so this is some filler text to push the code section below the summary. But instead of typing a whole lot of filler text, we can just add a read more tag to the content to override Hugo’s summary setting. ...
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Deploying on Netlify
July 30, 2017
copied from hugoDocs Netlify provides continuous deployment services, global CDN, ultra-fast DNS, atomic deploys, instant cache invalidation, one-click SSL, a browser-based interface, a CLI, and many other features for managing your Hugo website.
Assumptions You have an account with GitHub, GitLab, or Bitbucket. You have completed the Quick Start or have Hugo website you are ready to deploy and share with the world. You do not already have a Netlify account. ...
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Hello World
July 29, 2017
It Works!
This is a block of text it is not a bloc of text. This is a block of text it is not a bloc of text. This is a block of text it is not a bloc of text. This is a block of text it is not a bloc of text. This is a block of text it is not a bloc of text.
$ sudo make me a sandwich this is a blockquote ...
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Unsupervised Learning
July 1, 2017
{{multiple issues| {{Cleanup|date=May 2010}} {{More footnotes|date=February 2010}} }} {{Machine learning bar}}
”‘Unsupervised machine learning”’ is the [[machine learning]] task of inferring a function to describe hidden structure from “unlabeled” data (a classification or categorization is not included in the observations). Since the examples given to the learner are unlabeled, there is no evaluation of the accuracy of the structure that is output by the relevant algorithm—which is one way of distinguishing unsupervised learning from [[supervised learning]] and [[reinforcement learning]]. ...
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