Skip to content
TrackPodcasts
artsJul 18, 202520:38pending

Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms

About this episode

Provides an in-depth guide to neuroevolution, a field focusing on evolving artificial neural networks, primarily using the NEAT algorithm and its advanced derivatives like HyperNEAT and ES-HyperNEAT. It covers practical applications such as solving the XOR problem, pole-balancing, and autonomous maze navigation, highlighting the effectiveness of Novelty Search optimization in deceptive landscapes. The text also explores deep neuroevolution for reinforcement learning in complex environments like Atari games, detailing CNN architectures and unique genome encoding schemes. Additionally, it offers best practices, coding tips, and performance metrics for those implementing neuroevolutionary algorithms.

You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary

Get the Book now from Amazon:
https://www.amazon.com/Hands-Neuroevolution-Python-high-performing-neuroevolution-based-ebook/dp/B082J28SZ7?&linkCode=ll1&tag=cvthunderx-20&linkId=253a597add5fafba06c6ae92ac8c9868&language=en_US&ref_=as_li_ss_tl


Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy

Get every episode summarized

Each time CyberSecurity Summary publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.

Email me new episodes

Free for 3 shows. No card needed.

No transcript yet

This episode has not been transcribed. Request it and it moves to the front of the queue.

Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms

CyberSecurity Summary

0:00
20:38

More episodes

More from CyberSecurity Summary

View all episodes →