Learn Docker, Parti-corgi and Python

While "It works on my computer" might give you an excuse to close a bug ticket in the backlog, it doesn't really work when you're working on a team.

You need to ensure that your application can run regardless of what hardware your teammates are running or libraries they have installed.

Docker solves this problem by collecting everything your application needs to run into a container.

Containers aren't just for source code. They can hold configuration, scripts, and even their own filesystem. It's similar to a Virtual Machine, without having to bring a full operating system along for the ride.

Because containers run in isolation, there is some configuration involved to allow for files to be saved and containers to communicate with one another.

One of the most useful ways to get your head around Docker is to see it in action.

Follow along with Joel Lord as he works through the process of preparing an application to run in separate containers for the frontend and backend of an application that searches for gifs and re-encodes them with a caption supplied by the user.

You'll see how to configure the separate containers to communicate with one another, work with environment variables, and persist data to your local machine. Along the way, you'll pick up some tips on useful commands and bash scripting. Finally, you'll learn how to use Docker Compose to make it easy to run multiple containers simultaneously.


Docker containers wrap a piece of software in a complete filesystem that contains everything needed to run: code, runtime, system tools, system libraries – anything that can be installed on a server. This guarantees that the software will always run the same, regardless of its environment.

docker

Containerize Full-Stack JavaScript Applications with Docker

An inclusive community of content creators helping each other grow.

parti-corgi

How to Livestream Code and Design on Twitch

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

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

In this course we will take a simple Node.js app that connects to a MongoDB database and uses an Express web server, and learn how to setup a full software development deployment process as well as how to properly “Dockerize” the app. 

We’ll step through setting up proper version control techniques, managing configuring within the environment, setting up proper build/deploy/run processes, and all with abiding by Docker best practices. We’ll also make sure our deployment is setup for scale, and have consistent development practices in place for reliable app deployments.

Build a Twelve-Factor Node.js App with Docker

Zeit’s mission statement is to ‘Make Cloud computing as easy and accessible as Mobile computing.’ To that end, they’ve released some pretty amazing and powerful tools. In this course we’re going to take a look at `now`, their deployment tool, to see why it’s hands-down the simplest way to launch node applications, docker containers and even static files to the cloud.


We’ll also dig into some of now’s more advanced capabilities, including host aliasing, environment variables and integration with hosted databases.

Deploy Web Apps with Zeit Now

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

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

Sometimes you get strings instead of objects and you have to parse them out. Here's an example where the raw data I got from a Twitch chatbot message wasn't useful to me until I parsed it into a dictionary.

Turning a key/value string into a dictionary in Python

Kubernetes is an API to a computer. Multiple computers actually. Here we'll talk about what K8s is, how you can choose between the plethora of K8s related hosting options, and what the process looks like for getting some containers running.

WTF is Kubernetes (K8s)

Use [Hasura](https://hasura.io/), an open source GraphQL engine to setup a backend.

You need to have Docker installed. You can [install](https://docs.docker.com/install/) from the Docker website.

Set up a GraphQL Backend with Hasura and Docker

Many streamers use colorful banners on their stream pages that reflect their branding. Here's how you can create and use them.

* [Panel Maker](https://nerdordie.com/resources/free-resources/customizable-twitch-panels/)

Creating Panel Banners on Twitch

A short look at what I do before I change from my "Starting Soon" scene to my "live scene" and a brief overview of what to except from the collection

Introduction to Livestreaming

We cover some of the twitch interface and open a twitch account

Starting a Twitch account to livestream

We introduce OBS and compare it to SLOBS, download and set up our first scene including desktop capture and audio tips.

Introduction to OBS and creating your first scene

We cover how to get your stream key, how to use that stream key to test bandwidth and CPU usage settings, and go live on Twitch for the first time.

Going live on Twitch for the first time and testing bandwidth

While there are many useful tools and toys to pull together for a livestream, the truth is you don't need any of them to start.

What gear do I need to livestream?

It's likely that you will have many streams where no one is there, or people are there and they aren't telling you they are. Does it matter?

What if nobody comes to watch my livestream?

https://www.elgato.com/en/gaming/stream-deck

What is a Streamdeck?

[Streamlabs](https://streamlabs.com/) has two major halves: Streamlabs OBS, which is an OBS competitor, and Streamlabs-the-SaaS, which includes tools like chatbots, polls, giveaways, and loyalty rewards.

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