---
title: "Michael Munn, Sara Robinson og Valliappa Lakshmanan Machine Learning Design Patterns"
ean: "9781098115784"
brand: "Michael Munn, Sara Robinson og Valliappa Lakshmanan"
category: "Medier > Bøger"
price_min: 499
price_max: 499
currency: DKK
vendor_count: 1
in_stock: true
url: https://pricetracker.dk/p/9781098115784/machine-learning-design-patterns
last_checked: 2026-10-14
---

# Michael Munn, Sara Robinson og Valliappa Lakshmanan Machine Learning Design Patterns

EAN 9781098115784 · Mærke: Michael Munn, Sara Robinson og Valliappa Lakshmanan · Kategori: Medier > Bøger

Sammenlign priser på Michael Munn, Sara Robinson og Valliappa Lakshmanan Machine Learning Design Patterns fra 1 forhandler. Billigste pris nu: 499 kr hos Saxo DK. Se aktuelle priser og køb hos den billigste forhandler på https://pricetracker.dk/p/9781098115784/machine-learning-design-patterns

## Priser
- **Billigste pris nu:** 499 kr hos Saxo DK
- **Prisspænd:** 499 kr – 499 kr på tværs af 1 forhandler

## Forhandlere
| Forhandler | Pris | Levering |
|---|---|---|
| Saxo DK | 499 kr | — |

## Om produktet
Beskrivelse fra forhandleren Saxo DK:

> 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. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly

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Priserne er sidst kontrolleret 2026-10-14.