000			01464 a2200241 4500
003			IIITD
005			20240508020003.0
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
260			_aNew York : _bManning, _c©2020
300			_axxviii, 490 p. : _bill. ; _c24 cm.
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.
650			_a Machine learning.
650			_a Neural networks
700			_aAntiga, Luca
700			_aViehmann, Thomas
942			_2ddc _cBK _01
999			_c172611 _d172611