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    Modern Graph Theory Algorithms with Python. Harness the power of graph algorithms and real-world network applications using Python

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Colleen M. Farrelly, Franck Kalala Mutombo, Michael Giske
    Modern Graph Theory Algorithms with Python. Harness the power of graph algorithms and real-world network applications using Python Colleen M. Farrelly, Franck Kalala Mutombo, Michael Giske - okladka książki

    Modern Graph Theory Algorithms with Python. Harness the power of graph algorithms and real-world network applications using Python Colleen M. Farrelly, Franck Kalala Mutombo, Michael Giske - okladka książki

    Modern Graph Theory Algorithms with Python. Harness the power of graph algorithms and real-world network applications using Python Colleen M. Farrelly, Franck Kalala Mutombo, Michael Giske - audiobook MP3

    Modern Graph Theory Algorithms with Python. Harness the power of graph algorithms and real-world network applications using Python Colleen M. Farrelly, Franck Kalala Mutombo, Michael Giske - audiobook CD

    Serie wydawnicze:
    Learn
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    290
    Dostępne formaty:
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    Do przechowalni

    We are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale.
    This book guides you through the basics of network science, showing you how to wrangle different types of data (such as spatial and time series data) into network structures. You’ll be introduced to core tools from network science to analyze real-world case studies in Python. As you progress, you’ll find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop an epidemic spread. Later, you’ll learn about advanced techniques in network science, such as creating and querying graph databases, classifying datasets with graph neural networks (GNNs), and mining educational pathways for insights into student success. Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter.
    By the end of this book, you’ll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.

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    O autorach książki

    Colleen M. Farrelly is a lead data scientist and researcher with a broad industry background in machine learning algorithms and domains of application. While her focus has been industry, she also publishes academically in geometry, network science, and natural language processing.
    Colleen earned a graduate degree in Biostatistics from the University of Miami. Her work history includes fields like nuclear engineering, public health, biotechnology, retail, educational technology, and human behavior analytics. She previously published The Shape of Data, a comprehensive overview of machine learning from a geometric perspective. Colleen is currently focused on applications of generative models and tech education in the developing world
    Franck Kalala Mutombo is a Professor of Mathematics at Lubumbashi University and former Academic Director of AIMS-Senegal. He previously worked in a research position at Strathclyde University and at AIMS-South Africa in a joint appointment with the University of Cape Town. He holds a PhD in Mathematical Sciences (with focus in network science) from the University of Strathclyde, Glasgow, Scotland. His current research considers the impact of network structure on long-range interactions applied to epidemics, diffusion, object clustering, differential geometry of manifolds, finite element methods for PDEs, and data science. Currently, he teaches at University of Lubumbashi and across the AIMS Network.

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