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    Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autor:
    Jay Dawani
    Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks Jay Dawani - okladka książki

    Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks Jay Dawani - okladka książki

    Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks Jay Dawani - audiobook MP3

    Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks Jay Dawani - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    364
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models.
    You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application.
    By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.

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    O autorze książki

    Jay Dawani is a former professional swimmer turned mathematician and computer scientist. He is also a Forbes 30 Under 30 Fellow. At present, he is the Director of Artificial Intelligence at Geometric Energy Corporation (NATO CAGE) and the CEO of Lemurian Labs - a startup he founded that is developing the next generation of autonomy, intelligent process automation, and driver intelligence. Previously he has also been the technology and R&D advisor to Spacebit Capital. He has spent the last three years researching at the frontiers of AI with a focus on reinforcement learning, open-ended learning, deep learning, quantum machine learning, human-machine interaction, multi-agent and complex systems, and artificial general intelligence.

    Packt Publishing - inne książki

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