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You're reading from  Hands-On Neuroevolution with Python.

Product typeBook
Published inDec 2019
Reading LevelExpert
PublisherPackt
ISBN-139781838824914
Edition1st Edition
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Author (1)
Iaroslav Omelianenko
Iaroslav Omelianenko
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Iaroslav Omelianenko

Iaroslav Omelianenko occupied the position of CTO and research director for more than a decade. He is an active member of the research community and has published several research papers at arXiv, ResearchGate, Preprints, and more. He started working with applied machine learning by developing autonomous agents for mobile games more than a decade ago. For the last 5 years, he has actively participated in research related to applying deep machine learning methods for authentication, personal traits recognition, cooperative robotics, synthetic intelligence, and more. He is an active software developer and creates open source neuroevolution algorithm implementations in the Go language.
Read more about Iaroslav Omelianenko

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Concluding Remarks

In this chapter, we will summarize everything we have learned in this book and will provide further information so that you can continue your self-education. This chapter will help us revise the topics we have covered in a chapter-wise format and then provide a roadmap by sharing some details on Uber AI Labs, alife.org, and open-ended evolution at Reddit. We will also have a quick overview of the NEAT Software Catalog and the NEAT Algorithm Paper.

In this chapter, we will cover the following topics:

  • What we learned in this book
  • Where to go from here

What we learned in this book

Now that we have finished with the experiments, I hope that you have gained a solid understanding of the neuroevolution method of training artificial neural networks. We used neuroevolution to find solutions to a variety of experiments, from classic computer science problems to the creation of agents that are capable of playing Atari games. We also examined tasks related to computer vision and visual discrimination.

In this section, we will summarize what we learned in each chapter of this book.

Overview of the neuroevolution methods

In this chapter, we learned about the core concepts of genetic algorithms, such as genetic operators and genome encoding schemes.

We discussed two major genetic operators...

Where to go from here

We hope that your journey through the neuroevolution methods that were presented in this book was pleasant and insightful. We have done our best to present you with the most recent achievements in the field of neuroevolution. However, this field of applied computer science is developing rapidly, and new achievements are announced almost every month. There are many laboratories in universities, as well as in corporations around the globe, working on applying neuroevolution methods to solve tasks that are beyond the strength of mainstream deep learning algorithms.

We hope that you have become fond of the neuroevolution methods we discussed and are eager to apply them in your work and experiments. However, you need to continue your self-education to keep pace with the next achievements in the area. In this section, we will present some places where you can...

Summary

In this chapter, we briefly summarized what we learned in this book. You also learned about the places where you can search for further insights and continue your self-education.

We are happy to live in an era where the future becomes a reality at such a pace that we completely fail to notice the tremendous changes that happen in our life. Humanity is rapidly moving on a path to mastering the marvels of gene editing and synthetic biology. We continue to conquer the deep mysteries of the human brain, which opens the way for an ultimate understanding of our consciousness. Our advanced experiments in cosmology allow us to zoom closer and closer to the very first moments of the Universe.

We have built an advanced piece of mathematical apparatus that allows us to describe such mysteries as a neutrino that, on its path, can become an electron and after that, a neutrino again...

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Published in: Dec 2019Publisher: PacktISBN-13: 9781838824914
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Author (1)

author image
Iaroslav Omelianenko

Iaroslav Omelianenko occupied the position of CTO and research director for more than a decade. He is an active member of the research community and has published several research papers at arXiv, ResearchGate, Preprints, and more. He started working with applied machine learning by developing autonomous agents for mobile games more than a decade ago. For the last 5 years, he has actively participated in research related to applying deep machine learning methods for authentication, personal traits recognition, cooperative robotics, synthetic intelligence, and more. He is an active software developer and creates open source neuroevolution algorithm implementations in the Go language.
Read more about Iaroslav Omelianenko