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ePub + Kindle available Apr 16, 2024

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Inside

- Math, theory, and programming principles side by side
- Linear algebra, vector calculus and multivariate statistics for deep learning
- The structure of neural networks
- Implementing deep learning architectures with Python and PyTorch
- Troubleshooting underperforming models
- Working code samples in downloadable Jupyter notebooks

The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function.

- The core design principles of neural networks
- Implementing deep learning with Python and PyTorch
- Regularizing and optimizing underperforming models

Machine learning uses a cocktail of linear algebra, vector calculus, statistical analysis, and topology to represent, visualize, and manipulate points in high dimensional spaces. This book builds that foundation in an intuitive way–along with the PyTorch code you need to be a successful deep learning practitioner.

A thorough explanation of the mathematics behind deep learning!

Deep learning in its full glory, with all its mathematical details. This is the book!

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$49.99

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**all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!** - choose another
**free product**every time you renew - choose
**twelve free products**per year - exclusive
**50% discount**on all purchases -
*Math and Architectures of Deep Learning*ebook for**free**