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    Machine Learning for Finance. Principles and practice for financial insiders

    (ebook) (audiobook) (audiobook) Język publikacji: angielski
    Machine Learning for Finance. Principles and practice for financial insiders Jannes Klaas - okładka ebooka

    Machine Learning for Finance. Principles and practice for financial insiders Jannes Klaas - okładka ebooka

    Machine Learning for Finance. Principles and practice for financial insiders Jannes Klaas - okładka audiobooka MP3

    Machine Learning for Finance. Principles and practice for financial insiders Jannes Klaas - okładka audiobooks CD

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    Stron:
    456
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    Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.

    The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways.

    The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.

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    O autorze ebooka

    Jannes Klaas is a quantitative researcher with a background in economics and finance. He taught machine learning for finance as lead developer for machine learning at the Turing Society, Rotterdam. He has led machine learning bootcamps and worked with financial companies on data-driven applications and trading strategies.
    Jannes is currently a graduate student at Oxford University with active research interests including systemic risk and large-scale automated knowledge discovery.

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