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Deep learning with PyTorch

By: Contributor(s): Publication details: New York : Manning, ©2020Description: xxviii, 490 p. : ill. ; 24 cmISBN:
  • 9781617295263
Subject(s): DDC classification:
  • 006.32  STE-D
Contents:
Part 1. Core PyTorch. 1. Introducing deep learning and the PyTorch library 2. Pretrained networks 3. It starts with a tensor 4. Real-world data representation using tensors 5. The mechanics of learning 6. Using a neural network to fit the data 7. Telling birds from airplanes: learning from images 8. Using convolutions to generalize Part 2. Learning from images in the real world: early detection of lung cancer. 9. Using PyTorch to fight cancer 10. Combining data sources into a unified dataset 11. Training a classification model to detect suspected tumors 12. Improving training with metrics and augmentation 13. Using segmentation to find suspected nodules 14. End-to-end nodule analysis, and where to go next Part 3. Deployment. 15. Deploying to production.
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Holdings
Item type Current library Collection Call number Status Notes Date due Barcode Item holds
Books Books IIITD Reference Computer Science and Engineering CB 006.32 STE-D (Browse shelf(Opens below)) Checked out DBT Project Grant 06/06/2024 012944
Total holds: 0

Includes bibliographical references and index.

Part 1. Core PyTorch. 1. Introducing deep learning and the PyTorch library 2. Pretrained networks 3. It starts with a tensor 4. Real-world data representation using tensors 5. The mechanics of learning 6. Using a neural network to fit the data 7. Telling birds from airplanes: learning from images 8. Using convolutions to generalize Part 2. Learning from images in the real world: early detection of lung cancer. 9. Using PyTorch to fight cancer 10. Combining data sources into a unified dataset 11. Training a classification model to detect suspected tumors 12. Improving training with metrics and augmentation 13. Using segmentation to find suspected nodules 14. End-to-end nodule analysis, and where to go next Part 3. Deployment. 15. Deploying to production.

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