bookmarks (hide)615
- CVXPY creates powerful new PyTorch and TensorFlow layers
- notebook du papier https://arxiv.org/pdf/2004.08531.pdf
- 10-414/714: Deep Learning Systems
- IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz - GitHub - dynamicslab/databook_python: IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz
- https://github.com/advancedpythonprogramming
- data programming with TensorFlow to train arbitrary models like neural networks with only weak supervision.
- The Annotated Transformer
- Deep learning course CE7454, 2019. Contribute to xbresson/CE7454_2019 development by creating an account on GitHub.
- PyTorch tutorials demonstrating modern techniques with readable code - spro/practical-pytorch
- Complex domain recurrent neural network gating and Stiefel-manifold optimization in tensorflow - v0lta/Complex-gated-recurrent-neural-networks
publications
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- (2019)cite arxiv:1906.00910.
- (2016)cite arxiv:1606.04838.
- (2018)cite arxiv:1801.01944.
- CoRR, (2018)cite arxiv:1810.05095Comment: To appear on Nature Reviews Physics. The revised accepted version will be posted 6 months after publication.
- CoRR, (2018)
- Transactions of the International Society for Music Information Retrieval, 6 (1): 89–102 (2023)
- (2020)cite arxiv:2010.13581Comment: NeurIPS 2020. Code available at https://github.com/mfinzi/constrained-hamiltonian-neural-networks.
- (2019)cite arxiv:1910.05929Comment: 10 pages, 8 figures.
- Information, 14 (2): 137 (February 2023)
- (2014)cite arxiv:1406.7872Comment: Notes prepared to accompany a series of tutorial lectures given by the author at the 1st Lake Michigan Workshop on Combinatorics and Graph Theory, Western Michigan University, March 15--16 2014.
