Learn Faunadb, Python and Svelte

JavaScript community has been shaken recently by a release of Svelte 3 and for a good reason - it's a completely different approach than React or Vue. To quote the docs:

"[Svelte](https://svelte.dev/) is a radical new approach to building user interfaces. Whereas traditional frameworks like React and Vue do the bulk of their work in the browser, Svelte shifts that work into a compile step that happens when you build your app."

**The best part?** If you have 26 spare minutes you can learn the basics of Svelte 3 and start building your own apps with it!

Enjoy!



The magical disappearing UI framework

 - The web's JavaScript bloat crisis, solved. Svelte turns your templates into tiny, framework-less vanilla JavaScript.
 - Simple and familiar. Build apps out of composable, easy-to-write blocks using languages you already know.
 - Super fast, rock solid. Compile-time static analysis ensures the browser does no more work than it needs to.

svelte

Up and running with Svelte 3

This application serves as an introduction to building products with a JAMStack and Serverless approach. We cover everything from the beginning:

* starting with the Gatsby client
* handling authentication with Netlify Identity
* Shipping a GraphQL server on Netlify Functions
* Building in access control with a serverless capable database

It uses tools that remove as many of the barriers as possible while also allowing upgrading in the future. Netlify Functions can grow into AWS Lambda, Netlify Identity can grow into Auth0 or Cognito, and FaunaDB can grow into DynamoDB.

Gatsby is a blazing fast static site generator built with React. Gatsby gives you all of the modern JavaScript tools you want; set up and ready to go out of the box.

gatsby

Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB

This collection is a sequel to the [Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB](https://egghead.io/playlists/building-a-serverless-jamstack-todo-app-with-netlify-gatsby-graphql-and-faunadb-53bb) collection. In this collection we take the TODO application we built and convert it to run using Netlify Identity, AWS Lambda (using the serverless framework) and DynamoDB. We cover

* Fauna vs DynamoDB and when to use each
* Setting up AWS accounts
* Creating DynamoDB tables and data modeling differences between Fauna and Dynamo
* Converting our Netlify Functions deployment to a Serverless Framework deployment
* Implementing Custom authorizer functions on AWS

It uses tools that remove as many of the barriers as possible. Netlify Functions grows into Serverless Framework adn AWS Lambda, Netlify Identity is kept around, and FaunaDB can grows into DynamoDB.



FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

faunadb

Converting a Serverless App to run on AWS Lambda and DynamoDB with Serverless Framework

Neural networks, with Keras, bring powerful machine learning to Python applications. They can answer questions like “How much traffic will hit my website tonight?” or answer classification questions like “Will this customer buy our product?” or “Will the stock price go up or down tomorrow?”

In this course, we’ll build a fully connected neural network with Keras. This is the most basic type of neural network you can create, but it’s powerful in application and can jumpstart your exploration of other frameworks. 

We’ll start the course by creating the primary network. Then we’ll:

- build and configure the network, then evaluate and test the accuracy of each
- save the model and learn how to load it and use it to make predictions in the future
- expose the model as part of a tiny web application that can be used to make predictions

You don’t need to know a lot of Python for this course, but some basic Python knowledge will be helpful. Enjoy!

Python is a widely used dynamically typed programming language. It allows for many different programming paradigms including object-oriented, imperative, functional programming, and procedural styles.

python

Fully Connected Neural Networks with Keras

FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

Fauna is inspired by [Calvin](https://blog.acolyer.org/2019/03/29/calvin-fast-distributed-transactions-for-partitioned-database-systems/), has [built-in GraphQL integration](https://docs.fauna.com/fauna/current/api/graphql/), and a query language based on lambda calculus. Be sure to check out the [Jepsen analysis](https://jepsen.io/analyses/faunadb-2.5.4) and the associated [chart of consistency models](https://jepsen.io/consistency)

The Complete Guide to FaunaDB

In Introduction to Python, we will start at the beginning: Installing python 3 and using virtual environments. With a working environment, we will learn how to manipulate strings and numbers, and gain working knowledge of data structures and flow control. We will build on that by introducing modules and classes and learn how to interact with our python applications using command line switches, console input and files.

At the end of this course, you’ll have a great working knowledge of Python, fully capable of creating your own Python projects from scratch or jumping into an existing application with the capability to read, understand, and contribute to the application.

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/introduction-to-the-python-3-programming-language-notes).

Introduction to the Python 3 Programming Language

Artificial intelligence. Machine learning. Bots. Computers learning and communicating with us to do our bidding. But, where do you start? How do you get a machine to even begin to understand what you speak or type at it? There are several common machine learning algorithms that will help us begin to answer these questions.

In this course we’ll learn about common machine learning algorithms that don’t require implementing a neural network. We will not be going too much into the math behind them, but instead learn what each algorithm is good for, and how to train them. We'll also learn about a few metrics for evaluating models.

We’ll implement these in Python using `scikit-learn` using `scikit-learn`’s built-in data sets. The focus of this course is on implementation and a high-level understanding of these algorithms. 

We'll look at a few ways to evaluate our models, for both classification and regression models. We'll touch on mean squared error and coefficient of determination (for regression), and accuracy score, logarithmic loss, confusion matrices, and classification reports (for classification). 

Python 2.7 is used in the lesson videos but the code provided has Python 3 available. The only breaking change is the `print` statement API.

For additional information on installation, vocabulary, and common errors visit the `README.md` to the course code attached to each lesson.

Simple and efficient tools for data mining and data analysis

scikit-learn

Introductory Machine Learning Algorithms in Python with scikit-learn

AWS Amplify allows developers to create, configure, and deploy Serverless APIs and functions directly from their front-end environment.In this collection, you'll learn how to deploy various types of APIs and functions using the Amplify CLI. You'll also learn how to share code among various functions using Lambda layers and schedule functions to run at an interval to implement Serverless cron jobs.

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Up and Running with AWS Amplify Functions

Creating forms is one of the simplest and most complicated things at the same time. A basic form consists of many different things happening, we have to retain the value, we have to keep watch of validation and we have to show error messages when something is invalid, this is on both the fields and on the entire form itself if we send a request off to the backend service.

So we're going to create a form using just the normal svelte features. Then we'll look into creating a form using the svelte forms lib. 

Create forms in Svelte with Svelte Forms Lib

Learn how to create tuples in Python. The videos in this collection will also cover more advanced features of tuples

The Ins and Outs of Python Tuples

Svelte is a new approach to building user interfaces. 

Instead of doing the vast majority of work at the runtime as other frameworks do, Svelte shifts that work into the _compile_ step which happens when you build your app. As such, you're shipping heavily optimized code to your users.

In this quick lesson we are going to learn how to create a brand new project with Svelte 3 from scratch.

Create a new project with Svelte 3

In most Web applications we want to be able to render different HTML markup depending on values passed in from JavaScript code.

In this lesson we are going to learn how to render JavaScript variable in HTML markup when using Svelte 3.

Add data to HTML markup using Svelte 3

In Svelte 3, the same as in regular HTML we can add CSS styles to a component and this is what we're going to learn in this lesson.

Important note - styles are _scoped to the component_ which means that if we style a `h1` tag in component A, it won't automatically affect `h1` tags in component B.

Add styles to Svelte 3 component

It's highly unlikely that we'd like to have an entire app in a single file. Instead, what we'd like to do is to spread our code across multiple components and render them in one another.

In this lesson we are going to learn how to create a `<Surname />` Svelte 3 component which we're going to use in our main `App.svelte` file to display a user's surname.

Create a new Svelte 3 component to render it inside of another component

One of the key advantages of having a component system is the ability to render the component differently based on what kind of properties (shortened to 'props' in Svelte) were passed in to the component.

In this lesson we are going to learn how to declare properties inside of a Svelte component and how to pass in data to be rendered inside of a `<UserInfo />` component. We're also going to learn how to use default props which are used if a value for a property was not passed in.

Declare and pass in properties to a Svelte 3 component

Svelte automatically updates the DOM when your component's state changes. Often, some parts of a component's state need to be computed from other parts. This is where _reactive declarations_ come in.

In this lesson we are going to learn how to use reactive declarations to re-compute (and render) square of a counter value in a simple counter example whenever user clicks on a button.

Use reactive declarations to compute component state in Svelte 3

Quite often we need to be able to loop over and render a list of data that (for example) is returned from the backend. 

In this lesson we are going to learn how to use `each` block from Svelte to iterate over a list of data and render a list of European cities. We're also going to learn how to get the current `index` while iterating over a list, as well as how to destructure a currently iterated over element of the array.

Loop over and render a list of data using an `each` block in Svelte 3

The whole magic of webapps is that users can interact with our code via various DOM events and Svelte is no exception in that regard.

In this quick lesson we're going to learn how to use DOM events in Svelte 3 as well as how to use event _modifiers_ to alter DOM event behaviour (such as `once` and `preventDefault`)

Use DOM events and event modifiers in Svelte 3

Every Svelte component has a lifecycle that starts when it is created, and ends when it is destroyed. There are a handful of functions that allow you to run code at key moments during that lifecycle.

The one you'll use most frequently is `onMount`, which runs after the component is first rendered to the DOM. 

In this lesson we're going to learn how to use `onMount` to fetch and render data from Star Wars API.

Use an onMount lifecycle method to fetch and render data in Svelte 3

Not all application state belongs inside your application's component hierarchy. Sometimes, you'll have values that need to be accessed by multiple unrelated components, or by a regular JavaScript module.

In Svelte, we do this with _stores_. In this lesson we're going to learn how to create a `writable` store and how to use it to allow `Incrementer` and `Decrementer` components modify the value of the store

Use Svelte 3 stores to share data between multiple unrelated components

Sometimes we don't want to have a complicated `update` function to change a value of our store. We know exactly what we want to change the value to - we can just use the `store.set` function to do that.

In this lesson we're going to learn how to set a value to a Svelte 3 store by using the `store.set` function to set the value of a counter without having to increment/decrement it one by one.

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