| Edition |
First edition |
| Descript |
390 pages illustrations 23 cm |
| Note |
Includes index |
|
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.-- Source other than the Library of Congress |
| Subject |
Big data
|
|
Maskininlärning
|
|
Big data.
|
|
Machine learning.
|
| Classmark |
006.31
|
| Alt Auth |
Robinson, Sara
|
|
Munn, Michael
|
| ISBN/ISSN |
9781098115784 (pbk.) |
|
