 Courses from Hannah Davis

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

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

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

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

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

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

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

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

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

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 Language Processing in JavaScript with Natural

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


Using Cursor Chat, you can easily reference an entire directory of files and ask it to generate anything based on those files. This opens up the possibility of using your current code as a reference guide to create new code that follows existing patterns. With this approach, the result can be a polished prompt you can use with any AI to generate consistent and reliable output.

Focus less on crafting the perfect prompt upfront - what matters most is testing and iterating based on the AI's responses. The more results you see from the prompt, the more you can reinforce the good results and prune out the bad results until your prompt is polished enough that you can rely on the prompt to give you consistent quality results.


Create AI Prompts Based on a Directory of Files


With Cursor's "Composer" feature, you can select and entire directory and make changes to every file. This video walks through the process of selecting a folder and refactoring all the files from JavaScript to TypeScript. It also demonstrates other tips around generating terminal commands and customizing your main Cursor "Rules for AI".


Run an AI Refactor on Every File in a Directory with Cursor


This lesson breaks down the concept of asynchronous iterators and generators in JavaScript. Using the Node.js glob function as a practical example, you'll learn how Array.fromAsync facilitates the creation of arrays from asynchronous iterables and how generators can improve performance in file processing tasks.

Efficient File Processing in Node.js with Async Generators and Array.fromAsync

Learn the concept of asynchronous iterators and generators in JavaScript and Node.js Array.fromAsync


With the introduction of Svelte 5, the Svelte team has created a CLI, `sv`, that unifies all the tasks that previously spread across a handful of CLIS (create, add plugins, and migrate).

This lesson will create a Svelte project using the new `sv` CLI and build a counter component to showcase the new `$state`, `$derived`, and `$effect` runes that ship with the new version.



Get Started with Svelte 5 using the sv CLI

You'll create a SvelteKit project using the sv CLI and build a counter component showcasing Svelte $state, $derived, and $effect runes.



pnpm ships with a streamlined, easy way of managing Node.js versions for youself and your team. This video demonstrates why pnpm uses a specific version of Node.js, how to configure it globally, and how to set up your project so that anyone who is running your project will automatically use the same version of Node.js even if that version of Node.js is not installed.


use pnpm as a Node.js version manager

How you build with Angular has changed quite a lot over the years.

For example, the `inject` function was introduced in version 14 but you might still be familiar with Angular's constructor injection.

Similarly, structural directives like `ngif` or `ngfor` have been replaced with `@if` and `@for` blocks since version 17 and beyond.

These are patterns that need to be adjusted if you want to stay up-to-date with the latest Angular has to provide.

Luckily, Angular gives you great tools in the form of a migration CLI for supporting your migration efforts as you incrementally update your App from older version features and patterns. 

When you're done with this course you'll know how to target specific features, folders, and files to migrate to the latest Angular version.

You'll learn how to 'migrate':
- Structural directives with control flow syntax
- Constructor dependency inject to inject function
- Make components standalone
- Lazy load routes
- Input/Output decorators to signal-based API


Angular Migrations in Practice

Migrate old Angular patterns for version 18 and beyond

When we import assets in React components outside of the `public` folder that Nitro reads for assets by default when running the development server we need to tell Nitro how to find them with the `devProxy` configuration option.

```typescript
export default defineNitroConfig({
  renderer: './renderer.ts',
  devProxy: {
    '/app/assets': 'http://localhost:5173/app/assets'
  }
})
```

This is ultimately inadequate in scenarios where you want to import assets arbitrarily and have them "just work" but works in a pinch.



Use devProxy in Nitro Config to Serve Static Imported Assets in Dev Mode via Vite

Learn how to use devProxy in Nitro Config with Vite to serve static assets in React components in dev mode.

Start with a basic Vite React TypeScript app and enhance it for production use by integrating Nitro as the server. 

This requires a custom Vite plugin for nitro as well as a custom renderer and manually creating Nitro config.

The other way to approach this would be to add Vite to a Nitro app instead. Same basic result.

The [Nitro config reference](https://nitro.unjs.io/config) is a great read of you'd like to explore more options.


Configure a Vite React TypeScript Application on a Basic Nitro Server

Configure a Vite React TypeScript app by integrating Nitro for production, using custom plugins, renderers, and configs.

Quick Get Started style introduction to Vite using `pnpm create vite` with the React TypeScript template.

See [more in the official docs](https://vite.dev/guide/).

Create a Vite App with the React TypeScript Preset

Quick guide to start a Vite app using React TypeScript template with pnpm.

Over the years, your application have grown huge. Sometimes, really HUGE.

Many developers have contributed to the codebase. One day they pushed one coding style, a different one another day. Sometimes even different patterns and solutions for similar problems.

You want to keep your codebase consistent. In the Angular ecosystem, there were well established patterns for doing things the right way ™️. But the old solutions are being challenged by signal-based approach.

Eventually, you ask yourself the key question: **Should you integrate with the NEW solutions or stick to the battle-tested ones, but already considered LEGACY**?

That's a very good question indeed. Don't rush with architectural decisions 🤓.

## First, think of what problems you want to solve by introducing the change.

Do you have 1k LoC Angular components? Maybe even 2k? Do you and your team enjoy working with these? 😏

Where does the **domain logic and state management code** belong to - do you have a clear guidance on which code should be put where? Have you spot that different devs implement similar features in totally different ways?

Do you keep on asking the same questions over and over again: **whether this certain piece of code** should be **imperative** (plain old object properties and variables) or **reactive** (streams)? It'd be nice to keep all thinga reactive, but more often than not, it introduces complexity.

## Second, think of long-term consequences.

You are going to have more than one way to **manage the state**. But is that really an issue? If your codebase is modular, separate modules should not depend on each other in any way. Inner abstractions of one should be invisible to the other, etc.

You are going to introduce a **new tool**. On one hand, you need to learn it, and so does your team. But on the other hand, it's a breathe of fresh air. And it's useful to question the way you've been doing things so far. Maybe there's a more effective way?

You are going to embrace signal-based APIs. And that means taking taking advantage of Zoneless Angular. This means using all modern Angular APIs. And you know that signals are the future. Why would you want to stick to the **less performant, less type-safe, less flexible and, objectively, less effective solutions**?

## Finally, plan your INCREMENTAL integration.

INCREMENTAL is the absolute keyword here. You don't want to tell your PM you need to stop delivering features because of a refactor.

Actually, there's no need for a "refactor". Just pick the first new feature to be implemented. Or pick the one that needs to be extended.

NGRX Signal Store allows you to create both very simple & small stores, as well as bigger ones. **Composition** is the key here.

**Start simple**.

Create a `withState` section with the signals that hold the state. That's already reactive. And relatively simple.

```ts
const initialState: CartState = {
  items: ...,
  rebate: ...,
}

export const CartStore = signalStore(
  withState(initialState)
);
```

Create a `withMethods` section to provide ways to change the state:

```ts
export const CartStore = signalStore(
  ...
  withMethods((store) => ({
    addItem(item: ShoppingItem): void {
      patchState(store, (state) => ({ items: [ ...state.items, item ] }));
    },
    ...
);
```

Create a `withComputed` section to provide all derived computations:

```ts
export const CartStore = signalStore(
  ...
  withComputed(({ items }) => ({
    totalPrice: computed(() => items().reduce(...))
    ...
);
```

**Start simple**.

Now you can (literally) **`inject` this code into your existing solution**. All you need is to inject your brand new NGRX Signal Store implementation into a component, or a service:

```ts
@Injectable()
class ExistingBigService {
  cartStore = inject(CartStore)
  //...  
}
```

and that's it! What's beatiful is that NGRX Signal Store should remain an implementation details (of a component, service, etc). It **shouldn't leak outside to the entire application unintentionally**. Small NGRX Signal Stores help us to **keep logic more modular**.

Don't hesitate - give NGRX Signal Store a try! Start with the [_**Scalable Signals Angular Architecture with NGRX Signal Store**_](https://egghead.io/courses/scalable-signals-angular-architecture-with-ngrx-signal-store-a33abd39) course now!

Integrating NGRX Signal Store with an existing Angular application

Integrate NGRX Signal Store into your Angular app incrementally for modular, reactive state management. Improve consistency and performance with modern APIs.

Hannah Davis

Search Results