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Probabilistic deep learning : with Python, Keras, and TensorFlow Probability /
Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy...
| Auteurs principaux: | , , |
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| Format: | eBook |
| Langue: | Inglés |
| Publié: |
Shelter Island, New York :
Manning Publications,
[2020]
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| Sujets: | |
| Accès en ligne: | Texto completo (Requiere registro previo con correo institucional) |
| Résumé: | Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. |
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| Description: | "Exercises in Jupyter Notebooks"--Cover |
| Description matérielle: | 1 online resource |
| ISBN: | 9781638350408 163835040X 1617296074 9781617296079 |