Learn Cloudflare, Natural and Tachyons

Cloudflare Workers is a serverless application platform that enables you to create and deploy functions to Cloudflare's Edge Network.

Unlike other serverless providers that only have regional data centers, Cloudflare's Edge Network consists of hundreds of servers all over the world.

Cloudflare Workers is a powerful platform, and this course serves as a great introduction for what it can do.

Follow along with Kristian Freeman as you build a localization engine that renders data based on the Edge location nearest to the application's user.

After this course, you'll be ready to start experimenting with your own Cloudflare Workers projects. *Need an idea?* *[Check out the suggested Portfolio Project for this course.](https://egghead.io/projects/introduction-to-cloudflare-workers)*

Cloudflare secures and ensures the reliability of your external-facing resources such as websites, APIs, and applications. It protects your internal resources such as behind-the-firewall applications, teams, and devices. And it is your platform for developing globally-scalable applications.

cloudflare

Introduction to Cloudflare Workers

In this course we’ll work through Natural’s API for natural language processing in JavaScript. We’ll look at how to process text: learning how to break up language strings, find the word roots, work with inflectors, find sequences of words, and tag parts of speech. We’ll learn how to find important stats about a body of text: how to compare strings, how to classify text with machine learning, how to use the tf-idf tool to find relevant words. We’ll look at some of the extra tools Natural gives us, including the dictionary/thesaurus of WordNet, a phonetics comparer that lets us see if two words sound the same, and a spellcheck feature. We’ll also look at tries and digraphs, two data structures that help us better analyze bodies of text.

Natural is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.

natural

Natural Language Processing in JavaScript with Natural

Using NPM, you can install Cloudflare Workers' CLI (command-line interface), Wrangler, by typing `npm install -g @cloudflare/wrangler` into your command line. Wrangler allows you to generate and publish Cloudflare Workers projects. We'll also use the `wrangler login` command which will give you a link to authorize Wrangler. This will set up an API token specifically for your Wrangler installation that will allow you to publish and read all the Workers and Workers KV information that you need.

Install and Configure the Cloudflare Workers CLI Wrangler

To deploy a Cloudflare Workers serverless function, you'll need to sign up for a free Cloudflare Workers account at [workers.cloudflare.com](http://workers.cloudflare.com/), and select your unique workers.dev subdomain to deploy your functions to.

Create a Cloudflare Workers Account

Wranglers generate command, `wrangler generate`, allows you to create new Cloudflare Workers projects. Using the URL parameter, you can also generate new projects based on Cloudflare's starter templates, or open-source projects created by other developers, but for now, you will just use the default. To do this you'll only need to give it a name, for example, `wrangler generate my-worker`,  this will create a new project called my-worker. 

Generate New Cloudflare Workers Projects with Wrangler's generate Command

We'll take a look at the default code in `index.js` in a Workers project. We'll take a second to understand how it works before building the rest of our project. 

First will see what our Worker looks like in an actual browser by running `wrangler preview`. In a terminal, and we'll check out how Serverless functions in Cloudflare Workers listen for HTTPS requests via the "fetch" event and respond to them by creating and returning a new Response.

Write Your First Cloudflare Workers Serverless Function

Using `wrangler whoami` in a terminal we'll grab our account id so we can paste our id into the double quotes of `account_id = ""`  inside of `wrangler.toml` file. Using the `wrangler dev` command we can look at our Workers serverless function and test it before deploying it. Once we're ready to deploy, we can use the `wrangler publish` command to deploy it to our unique workers.dev subdomain.

Preview and Publish Your Cloudflare Workers Project

By updating the response body and setting a corresponding Content-Type header, we can render HTML pages from directly inside of our Cloudflare Workers function.

We will update our Cloudflare Worker to return a call to a template function that we define. That template function returns a string of HTML that we will render in the browser.

Render HTML Pages with Cloudflare Workers

Up until now, we've been rendering plain responses, not based on any information coming in with a request. The request.cf object sent with every incoming HTTP request to a Workers function has a ton of interesting information, including the regionality of the request, and which Cloudflare data center is responding to the request. Using this information, we'll render a dynamic HTML page based on where the user visiting our site is in the world.

Render Cloudflare Region Data for a Request Using request.cf

Deploying to your unique workers.dev subdomain is a great way to test your serverless functions before they're ready to go to production. Once you are ready to deploy to production, you can use Wrangler's environment feature to deploy to your zone, or Cloudflare-managed domain, by providing the "-e" (environment) flag when publishing.

You can check this worker out [here](https://hello-world-worker.signalnerve.com/).

Deploy to a Custom Domain with Cloudflare Wrangler Environments

We will learn about “stemming,” the process of finding the root of words, often in order to group words by a common base root. We will look at the Porter and Lancaster Stemmers, briefly touch on Natural’s support for Russian and Spanish stemmers, and introduce the function to stem and tokenize at the same time.

Find the roots of words using stemming in Natural

Inflectors are the modifiers of a word that indicate grammatical categories. While Natural’s coverage of inflectors is not comprehensive, we will show how Natural can pluralize/singularize nouns and count numbers. 

Pluralizing nouns and counting numbers with inflectors in Natural

N-grams are sequences of words, where the 'n' stands for the number of words in the sequence. In this lesson, we will see how to find bigrams (2-grams), trigrams (3-grams), and any other length n-gram in a body of text. 

Find sequences of words (n-grams) using Natural

We will learn how to compare how similar two strings are to each other, examining three algorithms: Jaro-Winkler, Levenshtein, and Dice’s Coefficient.  

You should note that none of these algorithms are inherently better than the others. Instead, it's important to choose the one that best fits your text data. 

Compare similarity of strings through string distance in Natural

In this lesson, we will learn how to train a Naive Bayes classifier or a Logistic Regression classifier - basic machine learning algorithms - in order to classify text into categories.

Classify text into categories with machine learning in Natural

In this lesson, we will learn how to train a Naive Bayes classifier and a Logistic Regression classifier - basic machine learning algorithms - on JSON text data, and classify it into categories.

While this dataset is still considered a small dataset -- only a couple hundred points of data -- we'll start to get better results.

The general rule is that Logistic Regression will work better than Naive Bayes, but only if there is enough data. Since this is still a pretty small dataset, Naive Bayes works better here. Generally, Logistic Regression takes longer to train as well. 

This uses data from Ana Cachopo: http://ana.cachopo.org/datasets-for-single-label-text-categorization

Classify JSON text data with machine learning in Natural

Tries are a data structure that provide an efficient way to search for the existence of a word or phrase in a body of text, or to search by prefix. 

Search more efficiently with tries using Natural

In this lesson, we’ll see how to use Natural’s probabilistic spell-checker, which uses the trie data structure. 

Include spell-check in text projects using Natural

Tf-idf, or term frequency-inverse document frequency, is a statistic that indicates how important a word is to the entire document. This lesson will explain term frequency and inverse document frequency, and show how we can use tf-idf to identify the most relevant words in a body of text. 

Identify the most important words in a document using tf-idf in Natural

Vue (pronounced /vjuː/, like view) is a progressive framework for building user interfaces. Unlike other monolithic frameworks, Vue is designed from the ground up to be incrementally adoptable. The core library is focused on the view layer only, and is very easy to pick up and integrate with other libraries or existing projects. On the other hand, Vue is also perfectly capable of powering sophisticated Single-Page Applications when used in combination with modern tooling and supporting libraries.

You can easily add CSS libraries to Nuxt using `yarn` or `npm` to install them, then simply adding them to the `nuxt.config.js` so they're included in each page. Then all the classes will be available for use in all of your templates. This lesson walks your through installing a library then adding it to your `nuxt.config.js`.

Add CSS Libraries to Nuxt

In this lesson, we’ll take a look at Natural’s phonetics feature. We’ll learn how to check whether two words sound alike, looking at both the SoundEx and Metaphone algorithms.

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