 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

When comparing two visually similar websites, it can often be challenging to precisely identify the subtle differences between them. To facilitate a rapid analysis of these discrepancies, a practical technique involves leveraging Chrome's integrated developer tools to capture full-page screenshots of each site. Subsequently, these screenshots can be uploaded to a platform like AI Studio. There, the AI can analyze the images and generate a report detailing the variations. This entire process, leveraging a few quick shortcuts, can be completed in under a minute, delivering a comprehensive breakdown of the differences for efficient comparison and analysis.

Quickly Compare Two Sites Using AI Studio with Chrome Full Page Screenshots

It's fairly trivial to create a React project, but there's always a big hurdle between creating it locally and making it shareable so that someone else can run it. This lesson walks you through the process of automating creating a React project locally, creating the repository where it's going to be hosted, and creating shareable links where it can be run so that you can quickly and easily create single one-off demos and shoot them over to your friends and coworkers for sharing your code ideas in a working environment.


```bash
share-react-project() {
  if [[ -z "$1" ]]; then
    echo "Usage: share-react-project <project_name>"
    return 1
  fi

  local project_name="$1"
  local github_username=$(gh api /user --jq '.login')

  echo "Creating Vite project: $project_name"
  pnpm create vite "$project_name" --template react

  cd "$project_name"

  echo "Initializing Git repository"
  git init

  echo "Adding all files to Git"
  git add .

  echo "Creating initial commit"
  git commit -m "Initial commit"

  local codesandbox_link="https://codesandbox.io/p/github/${github_username}/${project_name}"

  echo "Adding CodeSandbox link to README.md"
  echo "" >> README.md
  echo "## CodeSandbox" >> README.md
  echo "[![Open in CodeSandbox](https://assets.codesandbox.io/github/button-edit-blue.svg)](${codesandbox_link})" >> README.md

  echo "Adding README.md to Git"
  git add README.md

  echo "Committing README.md changes"
  git commit -m "Add CodeSandbox link"

  echo "Creating GitHub repository: $github_username/$project_name"
  gh repo create "$github_username/$project_name" --public

  echo "Pushing to remote 'origin'"
  git push -u origin main

  echo "Project '$project_name' created successfully!"
  echo "GitHub repository: https://github.com/$github_username/$project_name"
  echo "CodeSandbox link: $codesandbox_link"
}
```

Automate Creating a Local React Project, GitHub Repository, and Live Hosted Demo in a Single Command

[Verdaccio](https://verdaccio.org/) is a fully featured NPM registry that you can run locally on your machine. It’s a great tool for testing your release process, running end-to-end tests, and simulating real-world scenarios like deploying packages, installing them, and verifying functionality as an end user would.

Nx includes a [generator](https://nx.dev/features/generate-code) that simplifies setting up Verdaccio. To configure it, run:

```shell
npx nx g @nx/js:setup-verdaccio   
```

This command installs the necessary NPM packages and creates a `project.json` file with a `local-registry` target. To start the local registry, open a new terminal window and run:

```shell
npx nx local-registry
```

This will:
- Launch the Verdaccio registry.
- Configure NPM to point to the local Verdaccio server at `http://localhost:4873`.

> **Note:** A `project.json` file provides an advanced way to define scripts, offering more flexibility than a traditional `package.json` for managing complex targets.

Setup local Verdaccio server to release NPM packages

Learn how to spin up a local NPM registry powered by Verdaccio to test deploy your NPM packages.

In TypeScript, developers often begin by defining the structure of their data using type definitions or interfaces, which is a powerful way to ensure type safety. However, as applications grow and become more complex, simply defining the shape of an object might not be enough to guarantee its consistent creation and behavior throughout the codebase. This often leads to situations where objects that are intended to be of a certain "type" might be missing crucial properties or have incorrect data, which can be difficult to track down and debug.

This lesson tackles the question of when and why to move beyond basic type definitions and embrace classes in TypeScript. It demonstrates how classes provide a more robust mechanism for defining object structure, enforcing type constraints, and ensuring consistent object creation. By establishing a clear blueprint for objects, classes not only improve compile-time type safety but also offer runtime benefits and better code organization, ultimately helping developers avoid common pitfalls associated with loosely structured objects in larger TypeScript projects.

Stop Avoiding Classes in TypeScript

Karabiner isn't only for your keyboard. It can also hijack your mouse buttons and enable them to be similar to layers on your keyboard, where holding down a mouse button can allow you to tap keys on your keyboard to trigger commands and macros that can completely revolutionize your workflows. For example, you can hold down the right mouse button and tap on R to go to references in VS Code. Or hold down the right mouse button and tap the left mouse button to chop up videos and Screenflow.

This completely unlocks a whole new world of possibilities and use cases for your mouse as you can hover your mouse over anywhere on the page. And just tapping keys on your keyboard allows you to trigger mouse actions, to trigger keyboard actions, or to trigger macros and applications and terminal commands. and anything that your imagination can come up with. 

Karabiner: Rethinking Right-Click as a Macro Command Center

Make an HTML table more readable on mobile devices by using the ::before pseudo-element. We add labels to each cell, so they appear like cards when the table is viewed on smaller screens. The ::before content is set using a data-label attribute for each cell to enhance mobile viewability without horizontal scrolling.



Create a mobile friendly table with the before pseudo-element

Learn to use CSS ::before pseudo-element for mobile-friendly HTML tables. Add labels to cells for enhanced readability on mobile, avoiding horizontal scroll.

Any app of a decent size has event flows and messages being passed around. At a certain point it becomes complicated to explain it through prose and you want to turn towards diagrams. Now setting aside the time to create diagrams that accurately describe what's happening in your codebase can be quite time consuming. Luckily, with today's AI tools, we can now just do basic walkthroughs with rough recordings over codebase dictating what's going on along the way and then upload that to tools like AI Studio and then ask it to generate diagrams in a text-based format such as Mermaid that will then generate the flowcharts and diagrams for us. So this video walks us through this process of recording a rough video, uploading it to AI Studio and then asking AI Studio to generate the proper Mermaid diagram syntax.

Generate Mermaid Flow Chart Diagrams with Gemini 2.0 from Rough Screen Recordings

I'm a diehard Karabiner user and anytime I start doing something repetitively I always move it into my Karabiner configuration. Sometimes I'll fall into scenarios where I want to trigger a command, but I also want to know if that command succeeded or failed. Since Karabiner can trigger any command, setting up the command is fairly straightforward, but knowing whether it succeeded or failed became much more difficult. Luckily for me there is a package called terminal-notifier that you can install from homebrew and you can pipe the output of your command to terminal-notifier. And have a seamless way of triggering your keyboard shortcut which runs a command and you can be notified of the results.

Use Karabiner to Trigger a Terminal Command and Get Notified of the Results

There are a lot of scenarios when inspecting a site in Chrome where you don't want any click events to fire and you don't want any scripts to run. Or maybe something is animating in and out of the page and you want to capture it at a certain moment. This is a great use case for pausing script execution, which is kind of using the debugger manually by going into the Chrome DevTools and the Sources tab and clicking the pause button to essentially pause the entire page. This will give you a chance to inspect any of the elements and styles you want to inspect. And you don't have to worry about fire and click events or anything that might interrupt your investigation into the elements and styles that you want to find.

Pause Script Execution in Chrome to Easily Inspect Elements

Gemini 2.0 has a context window of a million tokens, and this means that you can upload hundreds of files and allow it to inspect and analyze massive refactors across entire code bases. 

This lesson demonstrates concatenating an entire code base into a single file and then uploading it to AI Studio. Once within AI Studio, we ask it to find the types of tasks that it can evaluate for us. Then we select one of the tasks and ask it to generate instructions for that task. Once those instructions are complete, we then turn them over to Windsurf's Cascade agent, and we allow Windsurf's Cascade to do all of the work for us as we sit back, relax, and let the AI do our jobs.

Hannah Davis

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