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 process Use Snorkel AI for weak
supervision and data programming Get code examples for using
Snorkel to label text and image datasets Use a weakly labeled
dataset for text and image classification Learn practical
considerations for using Snorkel with large datasets and using
Spark clusters to scale labeling
General
Imprint: |
O'Reilly Media
|
Country of origin: |
United States |
Release date: |
October 2021 |
Authors: |
Wee Hyong Tok
• Amit Bahree
• Senja Filipi
|
Dimensions: |
233 x 178 x 15mm (L x W x T) |
Format: |
Paperback
|
Pages: |
200 |
ISBN-13: |
978-1-4920-7706-0 |
Categories: |
Books
|
LSN: |
1-4920-7706-2 |
Barcode: |
9781492077060 |
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