Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
0 Hour(s) 47 Minute(s)
Language
English
Taught by
Ethan Kane
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
1 views
Course Overview
Learn to use an evolutionary algorithm to train and evolve efficient artificial neural networks
This is an introductory course to the NeuroEvolution of Augmenting Topologies algorithm. The course covers the most important concepts that characterize the NEAT algorithm, where a focus on understanding the theory behind genetic-algorithm-based artificial neural networks and their application to real-world problems, particularly in the fields of robotics and control.
This course is intended for individuals from all backgrounds and knowledge levels, as it is structured such that there are no advanced prerequisites. From the fundamental concepts of neural networks to the unique mechanisms found in the algorithm, the lectures provide a succinct and complete overview of NEAT that can be understood by any researcher, academic, or self-learner.
The list of topics covered include:
Introduction to neural networks
Introduction to genetic algorithms
Encoding
Reproduction/crossover
Mutation
Speciation
Dimensionality
Implementation
Application
This series also includes a tutorial on how to implement your own NEAT-based neural networks using a Python implementation of the algorithm. Only basic Python knowledge is required to get started on setting up the training environment and evolutionary process to procedurally generate efficient neural networks. All that is required is a simple code editor and your attention.
Taught by an academic researcher with advanced degrees, this course will familiarize you to NEAT, from how it works to how to use it to evolve your own neural networks.
Course Content
- 3 section(s)
- 8 lecture(s)
- Section 1 Background Theory
- Section 2 NEAT Theory
- Section 3 NEAT Application
What You’ll Learn
- Understand the mechanisms of genetic algorithms
- Understand the mechanisms of the NeuroEvolution of Augmenting Topologies algorithm
- Evolve NEAT-based artificial neural networks using NEAT-Python
- Apply NEAT to various control and computer problems
Skills covered in this course
Reviews
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EEndava SKD
This is too poor, I can't say that even the basics were covered. Only a small part of the original paper was covered (even then there are several detailed papers from the same author). The code explanation was without some benefits.
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DDevon k
Very useful. Excellent instructor
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JJill Wellman
For me Excellent. I went from a general understanding of neural networks, in particular, NEAT, to a better general understanding plus a detailed view of NEAT including code I can understand and run (if I can find it on getHub). Lectures were organized and clear. Slides worked well. Just plain Excellent.
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DDylan Kirk
Too much introduction and not enough meat. All of the code is simply provided, examples are not worked, the code is not well explained. Users are referred to the documentation page. I could have done all of this simply by reading the documentation.
