Isbn: 9781492077060 - practical weak supervision: doing more with less data (21 resultados)

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Paperback. Condición: New. Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi 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 have pursued 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 up to speed on the field of weak supervision, including ways to use it as part of the data science processUse Snorkel AI for weak supervision and data programmingGet code examples for using Snorkel to label text and image datasetsUse a weakly labeled dataset for text and image classificationLearn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling.…

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Paperback. Condición: new. Paperback. Build products using deep learning, weakly supervised learning, and natural language processing without collecting millions of training records. This practical book explains how and provides a how-to guide for actually shipping deep learning models--since most of these projects never leave the lab.Deep networks have enabled new applications using unstructured data to proliferate, but much of the work means collecting millions of records as well as labeled datasets. Author Russell Jurney from Data Syndrome helps machine-learning engineers, software engineers, deep learning engineers, and data scientists learn practical applications using several weakly supervised learning methods.You'll explore: Semi-supervised learning: Combine a small amount of labeled data with a large amount of unlabeled data to train an improved final modelTransfer learning: Re-train existing models from a related domain using training data from the problem domainDistant supervision: Combine low-quality labels from databases and other sources to create high-quality labels for the entire datasetModel versioning and management: start with a small labeled dataset and create a production grade model from concept through deployment Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Condición: New. Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive. There s a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Se.

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Paperback. Condición: new. Paperback. Build products using deep learning, weakly supervised learning, and natural language processing without collecting millions of training records. This practical book explains how and provides a how-to guide for actually shipping deep learning models--since most of these projects never leave the lab.Deep networks have enabled new applications using unstructured data to proliferate, but much of the work means collecting millions of records as well as labeled datasets. Author Russell Jurney from Data Syndrome helps machine-learning engineers, software engineers, deep learning engineers, and data scientists learn practical applications using several weakly supervised learning methods.You'll explore: Semi-supervised learning: Combine a small amount of labeled data with a large amount of unlabeled data to train an improved final modelTransfer learning: Re-train existing models from a related domain using training data from the problem domainDistant supervision: Combine low-quality labels from databases and other sources to create high-quality labels for the entire datasetModel versioning and management: start with a small labeled dataset and create a production grade model from concept through deployment Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: New. Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi 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 have pursued 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 up to speed on the field of weak supervision, including ways to use it as part of the data science processUse Snorkel AI for weak supervision and data programmingGet code examples for using Snorkel to label text and image datasetsUse a weakly labeled dataset for text and image classificationLearn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling.…

Taschenbuch. Condición: Neu. Neuware - Getting quality labeled data for supervised learning is an important step towards training performant machine learning models. In many real-world projects, getting labeled data often takes up significant amount of time. Weak Supervision is emerging as an important catalyst towards enabling data science team to fuse insights from labeling functions in order to produce weakly labeled datasets that can be used as inputs for machine learning and deep learning tasks.In this book, authors Amit Bahree, Senja Filipi, and Wee Hyong Tok from Microsoft help those working on machine learning projects face the typical challenges of getting good, quality labeled data for their projects. Readers will learn:- The lifecycle of enterprise machine learning projects, and the emerging area of weak supervision- How to use Snorkel for data labeling- How to use the weakly labeled dataset for Natural Language Processing- How to use the weakly labeled dataset for Computer Vision.…