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

    (ebook) (audiobook) (audiobook) Książka w języku angielskim
    Hands-On Neuroevolution with Python. Build high-performing artificial neural network architectures using neuroevolution-based algorithms Iaroslav Omelianenko - okladka książki

    Hands-On Neuroevolution with Python. Build high-performing artificial neural network architectures using neuroevolution-based algorithms Iaroslav Omelianenko - okladka książki

    Hands-On Neuroevolution with Python. Build high-performing artificial neural network architectures using neuroevolution-based algorithms Iaroslav Omelianenko - audiobook MP3

    Hands-On Neuroevolution with Python. Build high-performing artificial neural network architectures using neuroevolution-based algorithms Iaroslav Omelianenko - audiobook CD

    Autor:
    Iaroslav Omelianenko
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    368
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems.
    You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones.
    By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments.

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    O autorze książki

    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.

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