Deep learning with PyTorch
Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including th...
Bewaard in:
| Hoofdauteurs: | , , |
|---|---|
| Andere auteurs: | |
| Formaat: | Livre papier |
| Taal: | Anglais |
| Gepubliceerd in: |
Shelter Island (N.Y.) :
Manning Publications Co
C 2020.
|
| Onderwerpen: | |
| Autres localisations: | Voir dans le Sudoc |
| LEADER | 02932nam a22003257a 4500 | ||
|---|---|---|---|
| 001 | 848395 | ||
| 008 | 201214t20202020xxe ||| |||| 00| 0 eng d | ||
| 009 | PPN25113833X | ||
| 020 | |a 9781617295263 (br.) : |c 49.99 USD | ||
| 020 | |a 1617295264 (br.) | ||
| 024 | |a 9781617295263 | ||
| 041 | 0 | |a eng | |
| 082 | |a 006.31 | ||
| 100 | 1 | |a Stevens, Eli. | |
| 245 | 1 | 0 | |a Deep learning with PyTorch |c Eli Stevens, Luca Antiga, and Thomas Viehmann ; foreword by Soumith Chintala. |
| 260 | |a Shelter Island (N.Y.) : |b Manning Publications Co. | ||
| 260 | |c C 2020. | ||
| 300 | |a 1 vol. (XXVIII- 490 p.) : |b ill. en noir et blanc, fig., tabl., couv. ill. en coul. ; |c 24 cm. | ||
| 504 | |a Notes biblliogr. en bas de pages. Index | ||
| 505 | 0 | |a 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 | |
| 520 | |a Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning. | ||
| 650 | |a Apprentissage automatique | ||
| 650 | |a Apprentissage profond | ||
| 650 | |a Intelligence artificielle | ||
| 650 | |a Réseaux neuronaux (informatique) | ||
| 650 | |a Python (langage de programmation) | ||
| 700 | 1 | |a Antiga, Luca. |4 aut | |
| 700 | 1 | |a Viehmann, Thomas, |d 19..- |4 aut | |
| 700 | 1 | |a Chintala, Soumith. |4 aui | |
| 997 | |0 848395 |1 Livre papier |a Ressource papier |c 0/Orléans/ |c 1/Orléans/IDP/ |z Orléans, IDP, 9184 STE | ||