Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

Roque, Luís
Cerqueira, Vitor

Paperback

General Computers

ISBN10: 1805129236
ISBN13: 9781805129233
Publisher: Packt Publishing
Published: Mar 29 2024
Pages: 274
Weight: 1.05
Height: 0.58 Width: 7.50 Depth: 9.25
Language: English

Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes

Key Features
  • Learn the fundamentals of time series analysis and how to model time series data using deep learning
  • Explore the world of deep learning with PyTorch and build advanced deep neural networks
  • Gain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detection
  • Purchase of the print or Kindle book includes a free PDF eBook
Book Description

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.

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