Machine learning design patterns: solutions to common challenges in data preparation, model building, and MLOps
Intro -- Copyright -- Table of Contents -- Preface -- Who Is This Book For? -- What's Not in the Book -- Code Samples -- Conventions Used in This Book -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. The Need for Machine Learning Design Patterns -- What Are...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
October 2020
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| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | FHD01 UBR01 UBY01 UER01 |
| Zusammenfassung: | Intro -- Copyright -- Table of Contents -- Preface -- Who Is This Book For? -- What's Not in the Book -- Code Samples -- Conventions Used in This Book -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. The Need for Machine Learning Design Patterns -- What Are Design Patterns? -- How to Use This Book -- Machine Learning Terminology -- Models and Frameworks -- Data and Feature Engineering -- The Machine Learning Process -- Data and Model Tooling -- Roles -- Common Challenges in Machine Learning -- Data Quality -- Reproducibility -- Data Drift -- Scale -- Multiple Objectives -- Summary -- Chapter 2. Data Representation Design Patterns -- Simple Data Representations -- Numerical Inputs -- Categorical Inputs -- Design Pattern 1: Hashed Feature -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 2: Embeddings -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 3: Feature Cross -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 4: Multimodal Input -- Problem -- Solution -- Trade-Offs and Alternatives -- Summary -- Chapter 3. Problem Representation Design Patterns -- Design Pattern 5: Reframing -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 6: Multilabel -- Problem -- Solution -- Trade-Offs and Alternatives -- Design Pattern 7: Ensembles -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 8: Cascade -- Problem -- Solution -- Trade-Offs and Alternatives -- Design Pattern 9: Neutral Class -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 10: Rebalancing -- Problem -- Solution -- Trade-Offs and Alternatives -- Summary -- Chapter 4. Model Training Patterns -- Typical Training Loop |
| Beschreibung: | 1 Online-Ressource (xiv, 390 Seiten) Illustrationen |
| ISBN: | 9781098115753 |
Internformat
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| 520 | 3 | |a Intro -- Copyright -- Table of Contents -- Preface -- Who Is This Book For? -- What's Not in the Book -- Code Samples -- Conventions Used in This Book -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. The Need for Machine Learning Design Patterns -- What Are Design Patterns? -- How to Use This Book -- Machine Learning Terminology -- Models and Frameworks -- Data and Feature Engineering -- The Machine Learning Process -- Data and Model Tooling -- Roles -- Common Challenges in Machine Learning -- Data Quality -- Reproducibility -- Data Drift -- Scale -- Multiple Objectives -- Summary -- Chapter 2. Data Representation Design Patterns -- Simple Data Representations -- Numerical Inputs -- Categorical Inputs -- Design Pattern 1: Hashed Feature -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 2: Embeddings -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 3: Feature Cross -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 4: Multimodal Input -- Problem -- Solution -- Trade-Offs and Alternatives -- Summary -- Chapter 3. Problem Representation Design Patterns -- Design Pattern 5: Reframing -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 6: Multilabel -- Problem -- Solution -- Trade-Offs and Alternatives -- Design Pattern 7: Ensembles -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 8: Cascade -- Problem -- Solution -- Trade-Offs and Alternatives -- Design Pattern 9: Neutral Class -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 10: Rebalancing -- Problem -- Solution -- Trade-Offs and Alternatives -- Summary -- Chapter 4. Model Training Patterns -- Typical Training Loop | |
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Datensatz im Suchindex
| _version_ | 1804181953338933248 |
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| author | Lakshmanan, Valliappa Robinson, Sara Munn, Michael |
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| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | First edition |
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| isbn | 9781098115753 |
| language | English |
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| spelling | Lakshmanan, Valliappa Verfasser (DE-588)1222923750 aut Machine learning design patterns solutions to common challenges in data preparation, model building, and MLOps Valliappa Lakshmanan, Sara Robinson, and Michael Munn First edition Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly October 2020 1 Online-Ressource (xiv, 390 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Intro -- Copyright -- Table of Contents -- Preface -- Who Is This Book For? -- What's Not in the Book -- Code Samples -- Conventions Used in This Book -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. The Need for Machine Learning Design Patterns -- What Are Design Patterns? -- How to Use This Book -- Machine Learning Terminology -- Models and Frameworks -- Data and Feature Engineering -- The Machine Learning Process -- Data and Model Tooling -- Roles -- Common Challenges in Machine Learning -- Data Quality -- Reproducibility -- Data Drift -- Scale -- Multiple Objectives -- Summary -- Chapter 2. Data Representation Design Patterns -- Simple Data Representations -- Numerical Inputs -- Categorical Inputs -- Design Pattern 1: Hashed Feature -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 2: Embeddings -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 3: Feature Cross -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 4: Multimodal Input -- Problem -- Solution -- Trade-Offs and Alternatives -- Summary -- Chapter 3. Problem Representation Design Patterns -- Design Pattern 5: Reframing -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 6: Multilabel -- Problem -- Solution -- Trade-Offs and Alternatives -- Design Pattern 7: Ensembles -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 8: Cascade -- Problem -- Solution -- Trade-Offs and Alternatives -- Design Pattern 9: Neutral Class -- Problem -- Solution -- Why It Works -- Trade-Offs and Alternatives -- Design Pattern 10: Rebalancing -- Problem -- Solution -- Trade-Offs and Alternatives -- Summary -- Chapter 4. Model Training Patterns -- Typical Training Loop Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Entwurfsmuster (DE-588)4546895-3 gnd rswk-swf Electronic books Maschinelles Lernen (DE-588)4193754-5 s Entwurfsmuster (DE-588)4546895-3 s DE-604 Robinson, Sara Verfasser (DE-588)1222923904 aut Munn, Michael Verfasser (DE-588)122292398X aut Erscheint auch als Druck-Ausgabe 978-1-098-11578-4 |
| spellingShingle | Lakshmanan, Valliappa Robinson, Sara Munn, Michael Machine learning design patterns solutions to common challenges in data preparation, model building, and MLOps Maschinelles Lernen (DE-588)4193754-5 gnd Entwurfsmuster (DE-588)4546895-3 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4546895-3 |
| title | Machine learning design patterns solutions to common challenges in data preparation, model building, and MLOps |
| title_auth | Machine learning design patterns solutions to common challenges in data preparation, model building, and MLOps |
| title_exact_search | Machine learning design patterns solutions to common challenges in data preparation, model building, and MLOps |
| title_exact_search_txtP | Machine learning design patterns solutions to common challenges in data preparation, model building, and MLOps |
| title_full | Machine learning design patterns solutions to common challenges in data preparation, model building, and MLOps Valliappa Lakshmanan, Sara Robinson, and Michael Munn |
| title_fullStr | Machine learning design patterns solutions to common challenges in data preparation, model building, and MLOps Valliappa Lakshmanan, Sara Robinson, and Michael Munn |
| title_full_unstemmed | Machine learning design patterns solutions to common challenges in data preparation, model building, and MLOps Valliappa Lakshmanan, Sara Robinson, and Michael Munn |
| title_short | Machine learning design patterns |
| title_sort | machine learning design patterns solutions to common challenges in data preparation model building and mlops |
| title_sub | solutions to common challenges in data preparation, model building, and MLOps |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Entwurfsmuster (DE-588)4546895-3 gnd |
| topic_facet | Maschinelles Lernen Entwurfsmuster |
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