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Practical Weak Supervision /
Most data scientists and engineers today rely on quality labeled data to train their machine learning models. But building training sets manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Amit Bahree, Se...
| Autores principales: | , , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
O'Reilly Media, Inc.,
2021.
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a Most data scientists and engineers today rely on quality labeled data to train their machine learning models. But building training sets manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Amit Bahree, Senja Filipi, and Wee Hyong Tok from Microsoft show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies pursue ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get a practical overview of weak supervision Dive into data programming with help from Snorkel Perform text classification using Snorkel's weakly labeled dataset Use Snorkel's labeled indoor-outdoor dataset for computer vision tasks Scale up weak supervision using scaling strategies and underlying technologies. | ||
| 542 | |f Copyright © O'Reilly Media, Inc. | ||
| 550 | |a Made available through: Safari, an O'Reilly Media Company. | ||
| 588 | |a Online resource; Title from title page (viewed November 25, 2021) | ||
| 505 | 0 | |a Intro -- Copyright -- Table of Contents -- Foreword by Xuedong Huang -- Foreword by Alex Ratner -- Preface -- Who Should Read This Book -- Navigating This Book -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Introduction to Weak Supervision -- What Is Weak Supervision? -- Real-World Weak Supervision with Snorkel -- Approaches to Weak Supervision -- Incomplete Supervision -- Inexact Supervision -- Inaccurate Supervision -- Data Programming -- Getting Training Data | |
| 505 | 8 | |a How Data Programming Is Helping Accelerate Software 2.0 -- Summary -- Chapter 2. Diving into Data Programming with Snorkel -- Snorkel, a Data Programming Framework -- Getting Started with Labeling Functions -- Applying the Labels to the Datasets -- Analyzing the Labeling Performance -- Using a Validation Set -- Reaching Labeling Consensus with LabelModel -- Intuition Behind LabelModel -- LabelModel Parameter Estimation -- Strategies to Improve the Labeling Functions -- Data Augmentation with Snorkel Transformers -- Data Augmentation Through Word Removal -- Snorkel Preprocessors | |
| 505 | 8 | |a Data Augmentation Through GPT-2 Prediction -- Data Augmentation Through Translation -- Applying the Transformation Functions to the Dataset -- Summary -- Chapter 3. Labeling in Action -- Labeling a Text Dataset: Identifying Fake News -- Exploring the Fake News Detection(FakeNewsNet) Dataset -- Importing Snorkel and Setting Up Representative Constants -- Fact-Checking Sites -- Is the Speaker a "Liar"? -- Twitter Profile and Botometer Score -- Generating Agreements Between Weak Classifiers -- Labeling an Images Dataset: Determining Indoor Versus Outdoor Images | |
| 505 | 8 | |a Creating a Dataset of Images from Bing -- Defining and Training Weak Classifiers in TensorFlow -- Training the Various Classifiers -- Weak Classifiers out of Image Tags -- Deploying the Computer Vision Service -- Interacting with the Computer Vision Service -- Preparing the DataFrame -- Learning a LabelModel -- Summary -- Chapter 4. Using the Snorkel-Labeled Dataset for Text Classification -- Getting Started with Natural Language Processing (NLP) -- Transformers -- Hard Versus Probabilistic Labels -- Using ktrain for Performing Text Classification -- Data Preparation | |
| 505 | 8 | |a Dealing with an Imbalanced Dataset -- Training the Model -- Using the Text Classification Model for Prediction -- Finding a Good Learning Rate -- Using Hugging Face and Transformers -- Loading the Relevant Python Packages -- Dataset Preparation -- Checking Whether GPU Hardware Is Available -- Performing Tokenization -- Model Training -- Testing the Fine-Tuned Model -- Summary -- Chapter 5. Using the Snorkel-Labeled Dataset for Image Classification -- Visual Object Recognition Overview -- Representing Image Features -- Transfer Learning for Computer Vision -- Using PyTorch for Image Classification | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 700 | 1 | |a Bahree, Amit, |e author. | |
| 700 | 1 | |a Filipi, Senja, |e author. | |
| 710 | 2 | |a Safari, an O'Reilly Media Company. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781492077053/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6737970 | ||
| 994 | |a 92 |b IZTAP | ||