Artificial Intelligence & Machine Learning with Unity3D
Artificial Intelligence & Machine Learning with Unity3D, available at $34.99, has an average rating of 2.35, with 18 lectures, based on 13 reviews, and has 187 subscribers.
You will learn about How a neural-network works Set-up of the ML-Agents toolkit with Unity3D & Python Different machine-learning techniques: Reinforcement Learning & Imitation Learning Several practical examples: A.I. learns to play FlappyBird, Self-driving Car & 3D-Roller Ball Agent The ML-Agents components This course is ideal for individuals who are Anyone who wants to learn artificial intelligence in practice It is particularly useful for Anyone who wants to learn artificial intelligence in practice.
Enroll now: Artificial Intelligence & Machine Learning with Unity3D
Summary
Title: Artificial Intelligence & Machine Learning with Unity3D
Price: $34.99
Average Rating: 2.35
Number of Lectures: 18
Number of Published Lectures: 18
Number of Curriculum Items: 18
Number of Published Curriculum Objects: 18
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- How a neural-network works
- Set-up of the ML-Agents toolkit with Unity3D & Python
- Different machine-learning techniques: Reinforcement Learning & Imitation Learning
- Several practical examples: A.I. learns to play FlappyBird, Self-driving Car & 3D-Roller Ball Agent
- The ML-Agents components
Who Should Attend
- Anyone who wants to learn artificial intelligence in practice
Target Audiences
- Anyone who wants to learn artificial intelligence in practice
This crash-course is about machine Learning & Artificial Intelligence with Unity3D.
Why using Unity3D for Artificial Intelligence?
Unity3D is the perfect environment in order to train your own AIs. Let’s take the example of a Self-driving Car. What you need is complex environments where there are a lots of realistic physical interactions. You could provide these datas from interactions with the real world, but this is extreme inefficient and time consuming.
Since games become more and more realistic you can provide these informations from virtual environments. And for that Unity is perfectly positioned.
So, no matter if you are a game developer who wants to create AIs for games or if you are a hobby researcher who just want to play with machine Learning … The ML-Agents toolkit is the perfect start in order to create your own AIs.
What do we learn in this crash-course?
This course is structured into 4 major sections:
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Introduction
This section covers everything in order to get a quick start with the ML-Agents Toolkit. You will learn:-Set up of the ML-Agents toolkit with Tensorflow
-What is a neural-network?
-The Key Components of the Ml-Agents toolkit
-
3D Roller Ball AI
This lecture will give you a first impression of the Ml-Agents toolkit in practice. You will learn how to set up the environment and all the necessary components in order to train the AI.
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A.I. learns to play Flappy Bird
Instead wasting your time with playing this game, we will code our own A.I. that learns to play Flappy Bird by using Reinforcement Learning.
After training the AI is able to achieve an unlimited score in this game.
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Self-driving Car
The Self-driving Car is the probably the most famous example for Artificial Intelligence, so we will cover this as well. To train the Car we will use a technique called Imitation Learning.
Imitation Learning is special, because this method uses the inputs from a human Player in order to train the neural network.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Intro
Lecture 2: Set up of the ML-Agents Toolkit
Lecture 3: What is a Neural-Network?
Lecture 4: ML-Agents Key Components
Chapter 2: 3D Roller-Ball A.I – Reinforcement Learning
Lecture 1: 3D – RollerBall Project Overview
Lecture 2: RollerBall Set up
Lecture 3: Training the AI
Lecture 4: Project Files
Chapter 3: A.I. learns to play FlappyBird
Lecture 1: Flappy Bird Project Overview
Lecture 2: Explanation of the basic Scene
Lecture 3: Set Up of the ML Agents Components
Lecture 4: Training FlappyBird
Lecture 5: Project Files
Chapter 4: Self-driving Car – Imitation Learning
Lecture 1: Self-driving Car Overview
Lecture 2: Explanation of the basic Scene
Lecture 3: Set Up of the ML Agents Components
Lecture 4: Training the Car
Lecture 5: Project Files
Instructors
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ARdeveloper TT
Hobby Entwickler
Rating Distribution
- 1 stars: 5 votes
- 2 stars: 1 votes
- 3 stars: 1 votes
- 4 stars: 3 votes
- 5 stars: 3 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
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