Pytorch Deep Learning by Example¶
- 1. Credit and Attribution
- 2. Primer
- 3. Basic Neural Networks
- 4. Machine Learning in a Nutshell
- 5. Deep Learning for Computer Vision
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: large-scale machine learning on heterogeneous systems. 2015. Software available from tensorflow.org. URL: https://www.tensorflow.org/.
François Chollet. Deep Learning with Python. Manning Publications Co., USA, 1st edition, 2017. ISBN 1617294438.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. The MIT Press, 2016. ISBN 0262035618.
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M. Hessel, J. Modayil, H. van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. Azar, and D. Silver. Rainbow: Combining Improvements in Deep Reinforcement Learning. ArXiv e-prints, October 2017. arXiv:1710.02298.
Geoffrey E. Hinton and Ruslan R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786):504–507, July 2006. doi:10.1126/science.1127647.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: an imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019. URL: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.
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Need a diagram here
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Draw a block diagram.
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Insert image that shows the data splitting here.
(The original entry is located in /home/zafar/Desktop/PyTorchDL/Book/source/03-machine-learning/20-data.rst, line 412.)
Add exercises to compute the rest of the confusion matrix metrics
(The original entry is located in /home/zafar/Desktop/PyTorchDL/Book/source/03-machine-learning/25-model-evaluation.rst, line 68.)
Add exercises to compute the sensitivity after repeated tests
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(The original entry is located in /home/zafar/Desktop/PyTorchDL/Book/source/04-dl-computer-vision/20-transfer-learning.rst, line 19.)
Do we reall y need that?
(The original entry is located in /home/zafar/Desktop/PyTorchDL/Book/source/50-advanced/20-federated.rst, line 5.)