000 | 01464 a2200241 4500 | ||
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003 | IIITD | ||
005 | 20240910020004.0 | ||
008 | 240428b |||||||| |||| 00| 0 eng d | ||
020 | _a9781617295263 | ||
040 | _aIIITD | ||
082 |
_a006.32 _bSTE-D |
||
100 | _aStevens, Eli | ||
245 |
_aDeep learning with PyTorch _cby Eli Stevens, Luca Antiga, and Thomas Viehmann |
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260 |
_aNew York : _bManning, _c©2020 |
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300 |
_axxviii, 490 p. : _bill. ; _c24 cm. |
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501 | _aIncludes bibliographical references and index. | ||
505 |
_tPart 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 _tPart 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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650 | _a Machine learning. | ||
650 | _a Neural networks | ||
700 | _aAntiga, Luca | ||
700 | _aViehmann, Thomas | ||
942 |
_2ddc _cBK _02 |
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999 |
_c172611 _d172611 |