yellowbrick
loss-landscape
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| yellowbrick | loss-landscape | |
|---|---|---|
| 2 | 2 | |
| 4,194 | 2,642 | |
| 0.7% | - | |
| 2.8 | 0.0 | |
| 9 months ago | about 2 years ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
yellowbrick
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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Any interesting open projects to join? Or anyone want with some good ideas want to start one?
I have contributed to Yellowbrick in the past. https://github.com/DistrictDataLabs/yellowbrick/
loss-landscape
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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[D] Visualizing loss surface in input space
Code for https://arxiv.org/abs/1712.09913 found: https://github.com/tomgoldstein/loss-landscape
What are some alternatives?
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deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
itermplot - An awesome iTerm2 backend for Matplotlib, so you can plot directly in your terminal.
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both
seaborn-image - High-level API for attractive and descriptive image visualization in Python
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
fpdf2 - Simple PDF generation for Python
backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
scikit-survival - Survival analysis built on top of scikit-learn
cockpit - Cockpit: A Practical Debugging Tool for Training Deep Neural Networks