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    Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning Manu Joseph

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning Manu Joseph - okladka książki

    Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning Manu Joseph - okladka książki

    Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning Manu Joseph - audiobook MP3

    Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning Manu Joseph - audiobook CD

    Autor:
    Manu Joseph
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    552
    Dostępne formaty:
         PDF
         ePub
    We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML.

    This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability.

    By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.

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    O autorze książki

    Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies enabling digital and AI transformations, specifically in machine learning-based demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open-source contributor and developed an open-source library—PyTorch Tabular—which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son

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