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League of Legends Machine Learning with OCI - Introduction to Neural Networks
› Introduction
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 * -
   Introduction
    * Overview
   
    * Introduction to League of Legends
   
    * About Product/Technology
   
    * Objectives
   
    * OCI Elements
   
    * Annex - Additional Resources
   
    * Acknowledgements

 * +
   Get Started
    * Introduction
   
    * Task 1: Log in to Oracle Cloud
   
    * Acknowledgements

 * +
   Lab 1: Infrastructure
    * Introduction
   
    * Task 1: Cloud Shell
   
    * Task 2: Deploy with Terraform
   
    * Task 3: Start Deployment
   
    * Task 4: Accessing Notebook
   
    * Task 5: Setting up Data Science Environment
   
    * Task 6: Downloading DataSets
   
    * Task 7: Accessing our Notebooks
   
    * Acknowledgements

 * +
   Lab 2: Understand Neural Networks
    * Introduction
   
    * Task 0: What are Neurons
   
    * Task 1: What is a Neural Network
   
    * Task 2: Visualize a Neural Network
   
    * Task 3: Neural Networks Characteristics: Hyperparameters
   
    * Acknowledgements

 * +
   Lab 3: Build a Neural Network with fastai
    * Introduction
   
    * Task 1: League of Legends DataSets
   
    * Task 2: Build the "Offline" Neural Network
   
    * Task 3: Build the "Live Client" Neural Network
   
    * Task 4: Model Inference
   
    * Conclusions
   
    * Acknowledgements

 * +
   Need Help?
    * Introduction
   
    * Technical Requirements for Hands-on Labs
   
    * Get Your CloudWorld Oracle Cloud Free Tier Account
   
    * Your Oracle Account
   
    * How to use the LiveLabs Sandbox environment?
   
    * Hands-on Labs using SSH keys
   
    * Find other Hands-on Labs
   
    * CloudWorld Agenda
   
    * Visit the Oracle Community Theater and Community Lab!
   
    * Learn More
   
    * Acknowledgements


INTRODUCTION

Estimated Time: 5-10 minutes


OVERVIEW

One day, I woke up and said: how hard could it be to integrate Machine Learning
into Gaming, an industry where everything is already software? I started
researching the most popular games and, with some gaming experience I had
growing up, I decided to look deeper into League of Legends.

Long story short, after some months of developing my League of Legends API
wrapper (making calls to the official API with functions I created myself), I
started extracting data from professional players with the hopes of creating a
Machine Learning predictor that would tell me how I was performing in a match
during and after the match itself.

This is what this workshop is going to teach you.

We're going to create two models:

 * Offline Model: we'll obtain after-match data, and compare how well we did to
   pàst professional games. This will be a good model to theorize about which
   characters are good/bad in the long run.
 * Live Client Model: we'll obtain real-time data from a match, run it through
   our model, and return a winning probability (0-100)%.


Expand All Tasks


INTRODUCTION TO LEAGUE OF LEGENDS

League of Legends is a team-based strategy game in which two teams of five
powerful champions face off to destroy the other’s base. As a player, you can
choose from over 140 champions to make epic plays, secure kills, and take down
towers as you battle your way to victory. To win, you'll need to destroy the
enemy’s Nexus—the heart of each team's base.

Access and mobility play an important role in LoL. Your team needs to clear at
least one lane to access the enemy Nexus. Blocking your path are defense
structures called turrets and inhibitors. Each lane has three turrets and one
inhibitor, and each Nexus is guarded by two turrets. In between the lanes is the
jungle, where neutral monsters and jungle plants reside. The two most important
monsters are Baron Nashor and the Drakes. Killing these units grants unique
buffs for your team and can also turn the tide of the game.

Team composition depends on five positions. Each lane lends itself to certain
kinds of champions and roles—try them all or lock into the lane that calls you.
Champions get stronger by earning experience to level up and buy more powerful
items as the game progresses. Staying on top of these two factors is crucial to
overpowering the enemy team and destroying their base.





This image represents the final functionality of one of the two models we'll
explore in this workshop, where we use our already-trained ML model to make
real-time predictions about our in-game performances.

Here's a short 3-minute introductory video to League of Legends:








PREREQUISITES

 * An Oracle Free Tier, Paid or LiveLabs Cloud Account
 * Active Oracle Cloud Account with available credits to use for Data Science
   service.


ABOUT PRODUCT/TECHNOLOGY

OCI Data Science is a fully managed and serverless platform for data science
teams to build, train, and manage machine learning models using Oracle Cloud
Infrastructure.

The Data Science Service:

 * Provides data scientists with a collaborative, project-driven workspace.
 * Enables self-service, serverless access to infrastructure for data science
   workloads.
 * Helps data scientists concentrate on methodology and domain expertise to
   deliver models to production.


OBJECTIVES

In this lab, you will complete the following steps:

✓ Understand what a Neural Network is and how it works

✓ Creating an ML model

✓ Web Sockets / Data Streaming Techniques

✓ Integrating ML Models with Data Pipelines


OCI ELEMENTS

This solution is designed to work with several OCI services, allowing you to
quickly be up and running. You can read more about the services used in the lab
here:

 * OCI Data Science
 * OCI Cloud Shell
 * OCI Compute
 * OCI Autonomous JSON Database

You may now proceed to the next lab.


ANNEX - ADDITIONAL RESOURCES

If you have extra time after this workshop and want to get to know more about
League of Legends, we recommend reading these lists of articles to get a feel of
everything that can be done in the ML + Gaming space:

 1. Article 1: League of Legends Optimizer using Oracle Cloud Infrastructure:
    Data Extraction & Processing
 2. Article 2: League of Legends Optimizer using Oracle Cloud Infrastructure:
    Data Extraction & Processing II
 3. Article 3: League of Legends Optimizer using Oracle Cloud Infrastructure:
    Building an Adversarial League of Legends AI Model
 4. Article 4: League of Legends Optimizer using Oracle Cloud Infrastructure:
    Real-Time predictions
 5. Article 5: League of Legends Optimizer using Oracle Cloud Infrastructure:
    Real-Time predictions II


ACKNOWLEDGEMENTS

 * Author - Nacho Martinez, Data Science Advocate @ DevRel
 * Contributors - Victor Martin, Product Strategy Director
 * Last Updated By/Date - April 20th, 2023


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