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Time Series with PyTorch. Modern Deep Learning Toolkit for Real-World Forecasting Challenges Graeme Davidson, Lei Ma

Książka w języku angielskim angielski
Time Series with PyTorch. Modern Deep Learning Toolkit for Real-World Forecasting Challenges Graeme Davidson, Lei Ma - okladka książki

Time Series with PyTorch. Modern Deep Learning Toolkit for Real-World Forecasting Challenges Graeme Davidson, Lei Ma - okladka książki

Time Series with PyTorch. Modern Deep Learning Toolkit for Real-World Forecasting Challenges Graeme Davidson, Lei Ma - audiobook MP3

Time Series with PyTorch. Modern Deep Learning Toolkit for Real-World Forecasting Challenges Graeme Davidson, Lei Ma - audiobook CD

Autorzy:
Graeme Davidson, Lei Ma
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
606
Dostępne formaty:
     PDF
     ePub

Ebook
143,10 zł 159,00 zł (-10%)
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Do przechowalni

“The first book since my own to offer such comprehensive coverage of time series, with a deep learning-first perspective. For a holistic treatment of time series, this is the book for you.”

— Manu Joseph, author of Modern Time Series Forecasting with Python, creator of PyTorch Tabular

Neural networks are powerful for time-series forecasting, but using them effectively requires understanding architectures, training, and evaluation. This book combines these essentials with practical implementation.

Starting with PyTorch, you'll build neural networks from scratch, then explore recurrent networks, attention, transformers, and forecasting architectures including N-BEATS, N-HiTS, and the Temporal Fusion Transformer. You'll also learn hyperparameter tuning, conformal prediction for uncertainty, and reliable evaluation.

Beyond forecasting, the book explores transfer learning across series, diffusion-based synthetic data generation, and self-supervised representation learning. Later chapters cover classification, clustering, anomaly detection, and embeddings for large-scale time-series modeling.

Throughout, theory meets hands-on experimentation, helping you apply these methods confidently to real-world time-series problems.

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O autorach książki

Graeme Davidson is a data scientist working at one of the top demand forecasting platforms as rated by Gartner. He has over a decade of experience in analyzing and modeling with time-series data, from researching consumer motivations with Unilever and the University of Liverpool, to predicting consumer demand at Retail Express.
Lei Ma is a physicist-turned data scientist specializing in time series forecasting. He has tackled real-world forecasting challenges across industries like housing, logistics, ecommerce, and manufacturing. He has led and implemented numerous forecasting projects, and is not only experienced in building advanced time series models but also in providing strategic and holistic insights into time series analysis projects.

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