Deep Learning for Time Series Cookbook guides you with the basic strategies and tools you require to build effective Python and PyTorch solutions for your time series issues. Whether the goal is to predict future values, look for deviations in the data, or classify trends you encounter in your work, this book presents concise recipes for writing the code.
Over the course of the book, you’ll be introduced to time series analysis with simple and engaging examples and learn how to perform deep learning tasks using PyTorch. From knowing numerous characteristics of time series to data transformation for training transformers, you will have a glimpse of how to solve various problems. Regardless of whether you are a novice or a daily practitioner of machine learning, this ‘Deep Learning for Time Series Cookbook’ will equip you and enable you to perform time series tasks and build production quality forecasting solutions.
When you reach the end of the book Deep Learning for Time Series Cookbook, you will be equipped with the essential knowledge and skills to achieve valuable analysis and fruitful decisions from time series data through PyTorch's guide to deep learning.
Deep Learning for Time Series Cookbook Table of Contents:
- Getting Started with Time Series
- Getting Started with PyTorch
- Univariate Time Series Forecasting
- Forecasting with PyTorch Lightning
- Global Forecasting Models
- Advanced Deep Learning Architectures for Time Series Forecasting
- Probabilistic Time Series Forecasting
- Deep Learning for Time Series Classification
- Deep Learning for Time Series Anomaly Detection
Who is this course for?
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Most appropriate for machine learning aficionados and anyone who wants to develop a forecasting application using deep learning.
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