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    Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne - okladka książki

    Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne - okladka książki

    Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne - audiobook MP3

    Hands-On Graph Neural Networks Using Python. Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Maxime Labonne - audiobook CD

    Autor:
    Maxime Labonne
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    354
    Dostępne formaty:
         PDF
         ePub
    Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.
    Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.
    By the end of this book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.

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    O autorze książki

    Maxime Labonne is currently a senior applied researcher at Airbus. He received a M.Sc. degree in computer science from INSA CVL, and a Ph.D. in machine learning and cyber security from the Polytechnic Institute of Paris. During his career, he worked on computer networks and the problem of representation learning, which led him to explore graph neural networks. He applied this knowledge to various industrial projects, including intrusion detection, satellite communications, quantum networks, and AI-powered aircrafts. He is now an active graph neural network evangelist through Twitter and his personal blog.

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