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    Hands-On Deep Learning for Games. Leverage the power of neural networks and reinforcement learning to build intelligent games

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autor:
    Micheal Lanham
    Hands-On Deep Learning for Games. Leverage the power of neural networks and reinforcement learning to build intelligent games Micheal Lanham - okladka książki

    Hands-On Deep Learning for Games. Leverage the power of neural networks and reinforcement learning to build intelligent games Micheal Lanham - okladka książki

    Hands-On Deep Learning for Games. Leverage the power of neural networks and reinforcement learning to build intelligent games Micheal Lanham - audiobook MP3

    Hands-On Deep Learning for Games. Leverage the power of neural networks and reinforcement learning to build intelligent games Micheal Lanham - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    392
    Dostępne formaty:
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         ePub
         Mobi

    Ebook (107,10 zł najniższa cena z 30 dni)

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    29,90 zł

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    Do przechowalni

    The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development.
    We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments.
    As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.

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    O autorze książki

    Micheal Lanham is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.

    Micheal Lanham - pozostałe książki

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