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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.

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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

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

im·mu·ta·ble i(m)ˈmyo͞odəb(ə)l/ adjective - **unchanging over time or unable to be changed**.

Mutating data is the source of never ending headaches in software systems, and JavaScript code is no exception. Immutable data structures, pure functions, uni-directional data flow... you've probably been hearing these terms.

Immutable.js provides us the data structures and utilities that we need to start introducing this powerful concept of immutability into our applications.

Immutable.js provides many Persistent Immutable data structures including: List, Stack, Map, OrderedMap, Set, OrderedSet and Record.

immutable

Learn how to use Immutable.js

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

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

react

Sometimes our update logic is asynchronous. In this lesson we will investigate two ways in which we can write asynchronous update logic. 

…But, we will also discuss why asynchronous producers should be avoided, and how that can be done by better separating our logic. 

This lesson also demonstrates the “createDraft” and “finishDraft” APIs

Write Asynchronous Producers in Immer and Why You Shouldn’t

In this lesson we will send patches over a WebSocket to our server, and distribute them again from there.

Create a Simple WebSocket Server to Distribute Patches from Immer

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

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.

WTF is Streamlabs?

In this lesson, you'll learn how to deploy a serverless function using the Python runtime, connect it to an API endpoint, and interact with it from a React application.

Creating a full stack serverless app with Python, React hooks, and AWS Amplify Functions

Using the `open` function, and the `as` and `with` keywords, we'll open up and read from a file. At the end of this lesson, you will be able to loop through the lines of text in a file, process the text in Python, and then print them to the terminal.

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