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    Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    David Bellot, Dan Toomey
    Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R David Bellot, Dan Toomey - okladka książki

    Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R David Bellot, Dan Toomey - okladka książki

    Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R David Bellot, Dan Toomey - audiobook MP3

    Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R David Bellot, Dan Toomey - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    250
    Dostępne formaty:
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         ePub
         Mobi

    Ebook (29,90 zł najniższa cena z 30 dni)

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    Do przechowalni

    Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.
    We’ll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we’ll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you’ll see the advantage of going probabilistic when you want to do prediction.
    Next, you’ll master using R packages and implementing its techniques. Finally, you’ll be presented with machine learning applications that have a direct impact in many fields. Here, we’ll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.

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    O autorach książki

    David Bellot is a PhD graduate in computer science from INRIA, France, with a focus on Bayesian machine learning. He was a postdoctoral fellow at the University of California, Berkeley, and worked for companies such as Intel, Orange, and Barclays Bank. He currently works in the financial industry, where he develops financial market prediction algorithms using machine learning. He is also a contributor to open source projects such as the Boost C++ library.
    Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years, he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.

    David Bellot, Dan Toomey - pozostałe książki

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