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In this lesson we'll take some existing code and refactor it using some functions from the Ramda library, most notably,
converge. When we're done, we'll have taken a function with a couple of local variables and parameter references and converted it into more streamlined "point-free" or "tacit" functions.
When doing comparisons inside of functions, you end of relying heavily on the argument passed into the function. Ramda's
converge allows you to do comparisons in a Point-Free style allowing you more flexibility with composing and constructing functions. This lesson walks through refactoring a function to Point-Free style using Ramda's Converge.
You can really unlock the power of ramda (and functional programming in general) when you combine functions. Counting words in a string may seem like a relatively difficult task, but ramda makes it easy by providing a
countBy function. This lesson walks through using the
countBy to count words in a string.
fromPairs functions, along with the crucial
One of the more powerful functions for creating streams in MostJS is the
In this lesson, we use
As a bonus, we see what it looks like to use
Function.prototype.bind for a quick and dirty way to partially apply arguments to a function.
We don't always control the data we need in our applications, and that means we often find ourselves massaging and transforming our data. In this lesson, we'll learn how to transform objects in a declarative way using ramda's evolve function.
Public Class Fields allow you to add instance properties to the class definition with the assignment operator (
=). In this lesson, we'll look at their use case for simplifying event callbacks and state initialization with a React component.
In this lesson we'll take an array of objects and map it to a new array where each object is a subset of the original. We'll look at multiple ways to accomplish this, refactoring our code into a simple and easy to read function using Ramda's
get "trap" that will allow us to get items starting from the end of the array with ease.
Most of the functions offered by the ramda library are curried by default. Functions you've created or that you've pulled in from another library may not be curried. Ramda's
curryN functions allow you to take a non-curried function and use it as a curried functions. In the case where you have a manually curried function and you want to just call it like a normal function, you can use
uncurryN to get back a function that accepts all of the arguments at once.
Most provides many means for creating streams, the simplest of which is the
of function. In this lesson, we demonstrate the use of
of to lift a single value into a stream. We also show off the
just alias of
of, as well as, a common method for currying a function.
Learn how to use Object.assign to combine multiple objects together. This pattern is helpful when writing libraries that have a set of default options where you want to allow a user to override specific options. You'll also learn how to use a third party library to recursively merge nested objects together.
In Ember.js, functions that are executed through user interaction with your application are called actions, which can be defined at the route, controller, or component level. In this tutorial, we look at a simple example of defining actions on the controller and hooking it up to our template in order to update properties on the controller.
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.
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.
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.
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
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.
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.
ImmutableJS reduce functions can be nested to get data out of complex data structures. In this example, we will create an Immutable List from an Immutable Map that is nested inside of an Immutable List that is itself nested inside of an Immutable Map.
We can create the most basic components of our GraphQL Schema using GraphQL's Object Types. These types allow us to group related fields together under a specific type, such as a Video or a User, and then allows us to fetch these types when we query our schema. In this video, we'll learn how to write GraphQL Object Types in GraphQL's Schema language, as well as how to create resolvers for them, and ultimately how to query them.
In order to support mutations in Relay, there is a requirement that the GraphQL Server exposes mutation fields in a standardized way. This standard includes a way for mutations to accept and emit an identifier string, allowing Relay to track mutations and responses. In this video, we’ll learn how to use a helper available to us through
graphql-relay to create Mutation fields that accept
In order to properly traverse through collections, Relay-compliant servers require a mechanism to page through collections available in a GraphQL Schema. In this video, we’ll create a
Connection type from an existing GraphQL List Type and learn how to access edge information from each collection.
The GraphQL Relay Specification requires that a GraphQL Schema has some kind of mechanism for re-fetching an object. For typical Relay-compliant servers, this is going to be the
Node Interface. In this video, we’ll add in the
Node interface to a GraphQL Schema by using the helpers available in the
As we start building out more complex GraphQL schemas, certain fields start to repeat across different types. This is a perfect use-case for the Interface Type made available to us through GraphQL’s Type System. In this video, we’ll go over how to create an Interface Type and how to add it to an existing type in a GraphQL Schema.
In order to change the data that we can query for in a GraphQL Schema, we have to define what is called a
mutation in GraphQL. Mutations allow us to specify ways to create, update, and delete data. It also provides a way to fetch information after the mutation occurs. In this video, we’ll learn how to create a Mutation Type in GraphQL and specify the information we want returned.
When working with collections of things in GraphQL, we'll always reach out for the
GraphQLList Type. In this video, we'll learn how to use GraphQLList from the
graphql package in combination with a
GraphQLObject Type to create a field that returns a collection in our Schema.
In GraphQL, every field and nested object is able to take in arguments of varying types in order to do common operations like fetching an object by it's ID, filtering, sorting, and more. In this video, we'll update a field to take in an id argument and then learn how to use that argument in our resolve method to fetch a video by its id.
While certain fields in a GraphQL Schema can be optional, there are some fields or arguments that are necessary in order to either fulfill a query, or to provide a guarantee to people using the Schema that some field exists. In this video, we'll take a look at turning an argument in a NonNull argument by applying the GraphQLNonNull type in order to guarantee that the given argument is supplied in the query.
express-graphql package to serve up our GraphQL Schema as middleware, and also learn how to enable the GraphiQL tool in order to query our GraphQL Schema.