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Deep Learning for Time Series Cookbook. Use PyTorch and Python recipes for forecasting, classification, and anomaly detection Vitor Cerqueira, Luís Roque

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Deep Learning for Time Series Cookbook. Use PyTorch and Python recipes for forecasting, classification, and anomaly detection Vitor Cerqueira, Luís Roque - okladka książki

Deep Learning for Time Series Cookbook. Use PyTorch and Python recipes for forecasting, classification, and anomaly detection Vitor Cerqueira, Luís Roque - okladka książki

Autorzy:
Vitor Cerqueira, Luís Roque
Serie wydawnicze:
Cookbook
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
274
Dostępne formaty:
     PDF
     ePub

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Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise.
This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions.
By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.

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O autorze książki

​Vitor Cerqueira is a time series researcher with an extensive background in machine learning. Vitor obtained his Ph.D. degree in Software Engineering from the University of Porto in 2019. He is currently a Post-Doctoral researcher in Dalhousie University, Halifax, developing machine learning methods for time series forecasting. Vitor has co-authored several scientific articles that have been published in multiple high-impact research venues.

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