To get the most out of this book
We assume that you have basic knowledge of Python, data science, and machine learning. Coding and data manipulation using libraries such as NumPy or pandas should be familiar for a comfortable read. Readers should also know about basic concepts and techniques behind machine learning, including supervised and unsupervised learning, classification, regression, cross-validation, and evaluation.
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Software/hardware covered in the book |
OS requirements |
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Python (3.9) |
Windows, Mac OS X, or Linux (any) |
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PyTorch Lightning (2.1.2) |
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pandas (>=2.1) |
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scikit-learn (1.3.2) |
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NumPy (1.26.2) |
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torch (2.1.1) |
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PyTorch Forecasting (1.0.0) |
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GluonTS (0.14.2) |
Further requirements will be detailed in the introduction of the chapters.
If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.
Download the example code files
You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Deep-Learning-for-Time-Series-Data-Cookbook. If there’s an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
