Learn Greensock, Natural and Particle

Animations attract the eye and add a higher level of engagement for your users. But they’re not easy to set up, and they don’t always look the way they should on every browser. 

Greensock is the best platform available for creating performant, highly-customizable animations. This course will walk you through the features of Greensock, including how to:

- animate an element
- manually control an animation
- animate between CSS classes

…and more, with cross-browser support to ensure your animations look sharp everywhere.

Ultra high-performance, professional-grade HTML5 animation for the modern web

greensock

Create Amazing Animations with GreenSock

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

GreenSock supports all of the CSS3 3d properties, so you can freely rotate around X, Y, and Z axises with rotationX, rotationY, and rotationZ. Once that's set up, you can tweak the perspective of the element with transformPerspective.

Spin Elements in 3d with GreenSock

By default, elements use their own perspective, so if you rotate multiple elements on the page the perspective will seem a bit off for each of them. The solution is to set the perspective on the container element, then each child element will follow the parents' perspective.

Control the Shared 3d Perspective of Multiple Elements with GreenSock

Sometimes you want to leave an animation looping without any user interaction. This is done using the "repeat" and "yoyo" properties combined to keep the tween moving back and forth from the start properties to the end properties.

Loop a Tween Forever Using Yoyo and Repeat with GreenSock

The `gsap.set()` tween (or animation container) is a zero-duration tween that instantly applies a property to an HTML element. In addition, there is a subtle but important difference between `opacity` and Greensock's `autoAlpha` properties. You will learn how to hide a menu on page load and also how to prevent the element from flashing on page load. This lesson contains Greensock's basic building blocks that are used to create advanced animations with UI elements.

To add the latest Greensock library to your project, go to [Greensock's website](https://greensock.com/). Fun fact: GSAP stands for Greensock Animation Platform, and their current latest version is GSAP 3!

A common programming convention for Greensock I like to follow is, if you're going to animate a property with Greensock, keep it in Greensock, otherwise, if you are not going to animate a property, keep it in your CSS.

Hide a Menu with GSAP3's set() Tween and autoAlpha Property

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

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.

Check if words sound alike using Natural

This lesson introduces WordNet, which is an important resource in natural language processing. With WordNet, we can look up a word’s definition, or find its synonyms.

Find a word’s definition using WordNet in Natural

An important component of many natural language processing projects is being able to identify the grammar of a piece of text. We’ll learn how to do that with Natural’s parts of speech (POS) tagger.  

There are many tags, and it's worth looking them up online (search "POS tag symbols") to become familiar with them all. 

The setup of the tagger may seem a little strange, but it allows you to replace the lexicon or the rules with a different lexicon or rule set of your choice. 

Tag parts of speech using Natural

By this point we've seen that classification can take a long time, and with more data, it would take even longer. Luckily, Natural provides support to save your classifiers. In this lesson, we will learn how to save a classifier and load it into a new project in order to classify new data.

Using machine learning classifiers in a new project

A part of Natural Language Processing (NLP) is processing text by “tokenizing” language strings. This means we can break up a string of text into parts by word, sentence, etc. In this lesson, we will use the `natural` library to tokenize a string. First, we will break the string into words using `WordTokenizer`, `WordPunctTokenizer`, and `TreebankWordTokenizer`. Then we will break the string into sentences using `RegexpTokenizer`.

Break up language strings into parts using Natural

In this lesson, we will look at Greensock's Draggable API. We will implement a scrollable `<div>` container and explore some options available.

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