Practical weak supervision : (Record no. 80287)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01792nam a22002057a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220823b2022 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789355420435 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 TOK-W |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Tok, Wee Hyong |
| 245 ## - TITLE STATEMENT | |
| Title | Practical weak supervision : |
| Remainder of title | doing more with less data / |
| Statement of responsibility, etc. | Wee Hyong Tok, Amit Bahree and Senja Filipi |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | Shroff Publishers |
| Date of publication, distribution, etc. | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 169 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 900.00. |
| 500 ## - GENERAL NOTE | |
| General note | 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.<br/><br/>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.<br/><br/>Get up to speed on the field of weak supervision, including ways to use it as part of the data science process<br/>Use Snorkel AI for weak supervision and data programming<br/>Get code examples for using Snorkel to label text and image datasets<br/>Use a weakly labeled dataset for text and image classification<br/>Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Supervised learning (Machine learning) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Natural language processing (Computer science) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer vision |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Bahree, Amit |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Filipi, Senja |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Current library | Shelving location | Date acquired | Full call number | Barcode | Date last seen | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | 003-007 | BITS Pilani Hyderabad | General Stack (For lending) | 23/08/2022 | 006.31 TOK-W | 46055 | 13/11/2024 | Books |