Artificial Intelligence for Simple Games

Artificial Intelligence for Simple Games
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 5.01 GB
Genre: eLearning Video | Duration: 118 lectures (12 hours, 21 mins) | Language: English

Learn how to use powerful Deep Reinforcement Learning and Artificial Intelligence tools on examples of AI simple games!

What you'll learn

SOLVE THE TRAVELLING SALESMAN PROBLEM
Understand and implement Genetic Algorithms
Get the general AI framework
Understand how to use this tool to your own projects
SOLVE A COMPLEX MAZE
Understand and implement Q-Learning
Get the right Q-Learning intuition
Understand how to use this tool to your own projects
SOLVE MOUNTAIN CAR FROM OPENAI GYM
Understand and implement Deep Q-Learning
Build Artificial Neural Networks with Keras
Use the environments provided in OpenAI Gym
Understand how to use this tool to your own projects
SOLVE SNAKE
Understand and implement Deep Convolutional Q-Learning
Build Convolutional Neural Networks with Keras
Understand how to use this tool to your own projects

Requirements

High school maths
Basic knowledge of programming, such as "if" conditions, "for" and "while" loops, etc.

Description

Ever wish you could harness the power of Deep Learning and Machine Learning to craft intelligent bots built for gaming?

If you’re looking for a creative way to dive into Artificial Intelligence, then ‘Artificial Intelligence for Simple Games’ is your key to building lasting knowledge.

Learn and test your AI knowledge of fundamental DL and ML algorithms using the fun and flexible environment of simple games such as Snake, the Travelling Salesman problem, mazes and more.

1. Whether you’re an absolute beginner or seasoned Machine Learning expert, this course provides a solid foundation of the basic and advanced concepts you need to build AI within a gaming environment and beyond.

2. Key algorithms and concepts covered in this course include: Genetic Algorithms, Q-Learning, Deep Q-Learning with both Artificial Neural Networks and Convolutional Neural Networks.

3. Dive into SuperDataScience’s much-loved, interactive learning environment designed to build knowledge and intuition gradually with practical, yet challenging case studies.

4. Code flexibility means that students will be able to experiment with different game scenarios and easily apply their learning to business problems outside of the gaming industry.

‘AI for Simple Games’ Curriculum

Section #1 — Dive into Genetic Algorithms by applying the famous Travelling Salesman Problem to an intergalactic game. The challenge will be to build a spaceship that travels across all planets in the shortest time possible!

Section #2 — Learn the foundations of the model-free reinforcement learning algorithm, Q-Learning. Develop intuition and visualization skills, and try your hand at building a custom maze and design an AI able to find its way out.

Section #3 — Go deep with Deep Q-Learning. Explore the fantastic world of Neural Networks using the OpenAI Gym development environment and learn how to build AIs for many other simple games!

Section #4 — Finish off the course by building your very own version of the classic game, Snake! Here you’ll utilize Convolutional Neural Networks by building an AI that mimics the same behavior we see when playing Snake.

Who this course is for:

Anyone interested in beginning their AI journey
Anyone interested in creating an AI for games
Anyone looking for flexible tools to solve many kinds of Artificial Intelligence problems
A data science enthusiast looking to expand their knowledge of AI