Deep learning with PyTorch
Publication details: New York : Manning, ©2020Description: xxviii, 490 p. : ill. ; 24 cmISBN:- 9781617295263
- 006.32 STE-D
Item type | Current library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|---|
Books | IIITD Reference | Computer Science and Engineering | CB 006.32 STE-D (Browse shelf(Opens below)) | Checked out | DBT Project Grant | 21/10/2024 | 012944 |
Browsing IIITD shelves, Shelving location: Reference, Collection: Computer Science and Engineering Close shelf browser (Hides shelf browser)
CB 006.31 SUT-R Reinforcement learning : an introduction | CB 006.31 TRA-D Grokking deep learning | CB 006.312 WIC-R R for data science : | CB 006.32 STE-D Deep learning with PyTorch | CB 006.35 RAV-G Getting started with Google BERT : build and train state-of-the-art natural language processing models using BERT | CB 006.35 RAV-G Getting started with Google BERT : build and train state-of-the-art natural language processing models using BERT | CB 006.4 BIS-P Pattern recognition and machine learning |
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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