- Introduction
- Source code
- Feedback and support
- What is Neuroevolution?
- Artificial Neural Networks
- Neuralevolution training
- NeuroEvolution library
- Visualise the network behaviour during the training
What you'll learn
- How Evolutionary algorithms works
- Artificial Neural Networks
- How to train a model to play different games
- Alternative way to train Artificial Neural networks
Description
Neuroevolution is a powerful approach to machine learning and artificial intelligence that uses evolutionary algorithms to evolve neural networks.
Most neural networks use gradient descent rather than neuroevolution. However, around 2017 researchers at Uber stated they had found that simple structural neuroevolution algorithms were competitive with sophisticated modern industry-standard gradient-descent deep learning algorithms.
Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
This course introduces students to the principles of neuroevolution and the techniques used to design and implement neuroevolution algorithms.
The course covers the following topics:
Introduction to neuroevolution: basic principles and applications
Evolutionary algorithms: genetic algorithms, genetic programming, and evolutionary strategies
Neural networks: types, architectures, and training techniques
Neuroevolution algorithms: evolutionary algorithms applied to neural networks
Applications of neuroevolution: games, and optimization problems
Advanced topics: multi-objective neuroevolution, neuroevolution of recurrent neural networks, and deep neuroevolution.
In this project, we have applied GeneticEvolution to multiple games such as self-driving cars, smart caps and flappy bird.
This course is a follow-up to my other course about Artificial Neural Networks from scratch, where I show how to create an ANN from scratch without libraries. In that project, the learning process is done using backpropagation(gradient descent), this project uses a different approach. We will use Evolutionary Algorithm.
By following this course until the end, students will have a solid understanding of the principles of neuroevolution and the ability to design and implement neuroevolution algorithms for a variety of applications.
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About the instructors
- 4.21 Calificación
- 18825 Estudiantes
- 5 Cursos
Alexsandro Souza
Tech Lead
Alexsandro is a writer, instructor and open source contributor. With over 10 years of experience in the Software Development industry, he has been employed by companies worldwide, during which, he led many teams on a variety of projects.
To learn more about his growing skillset and experience follow him on the social media
Student feedback
Course Rating
Reviews
Diving into "Deep Neuroevolution" was transformative. It opened new vistas in AI by leveraging evolutionary algorithms to sculpt neural networks. From fundamentals to practical projects like self-driving cars and Flappy Bird, it balanced theory with hands-on experience. Transitioning seamlessly from prior courses, it broadened my toolkit. By the end, I gained a solid grasp of neuroevolution principles and the ability to implement diverse algorithms. An indispensable journey for AI enthusiasts.
Very interesting. Hope you produce more courses on Neuroevolution please!
Perfect quick reference for getting started with neuro evolution and ANN. This without being overwelmed by multiple documentations that could be intimidating when trying to learn the basics of deep learning !
Compact and clear explanation on the topic of Neuroevolution.
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I am honored to be given the opportunity to understand this course. thank you