deepchecks
WeightWatcher
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| deepchecks | WeightWatcher | |
|---|---|---|
| 15 | 4 | |
| 3,338 | 1,389 | |
| 2.8% | 1.3% | |
| 8.6 | 9.2 | |
| 6 days ago | 12 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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.
deepchecks
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Detect, Defend, Prevail: Payments Fraud Detection using ML & Deepchecks
Also if you have any confusion related to it. You can directly go to their discussion section in github :
- Deepchecks: Open-source ML testing and validation library
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Deepchecks' New Open Source is on Product Hunt, and Needs Your Help
GitHub for Deepchecks: https://github.com/deepchecks/deepchecks
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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Data Validation tools
I use DeepChecks for my continuous training pipelines. You can check out the Data Integrity Checks.
- Deepchecks
- deepchecks: Test Suites for Validating ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.
- QA help comes in many forms: Sometimes, from your heavily funded competitor
- Deepchecks: An open-source tool for testing machine learning models and data
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Test suites for machine learning models in Python (New OSS package)
And if you liked the project, we'll be delighted to count you as one of our stargazers at https://github.com/deepchecks/deepchecks/stargazers!
WeightWatcher
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Ask HN: Have you seen anything original produced by generative AI?
These models are pretty much always extrapolating [0]
Whether the extrapolation is crude/low-rank or astute/high-rank is a question of memorization vs generalization. That gets into the question of whether or not the model is over-fitted or under-fitted. There are certain heuristics borrowed from high dimensional statistical physics that can be used to guess how good the test performance of a model will be on a typical task without even knowing what the test data is [1].
Originality for me means finding better answers to sub-tasks, and then combining those answers together in a better way. This is the nirvana of cross-entropy minimization - the emergence of capability results from gaining the ability to amass a wider range of skills, improving upon them, and percolating those improvements towards multiply the leverage of other skills.
How long such a thing can keep improving with current tech, who knows, but you should really think critically about whether that sounds just like interpolation through the corpus.
[0] Learning in High Dimension Always Amounts to Extrapolation - https://arxiv.org/abs/2110.09485
[1] https://github.com/CalculatedContent/WeightWatcher
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Physics and Machine Learning
One of the things I love about physics is that, in addition to probably being my favorite of study in it's own right, it seems that a lot of the conceptual/mathematical content carries over and contributes to other fields. One example I've come across recently can be found here: https://github.com/CalculatedContent/WeightWatcher and here:
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
- A New Link to an Old Model Could Crack the Mystery of Deep Learning
What are some alternatives?
great_expectations - Always know what to expect from your data.
captum - Model interpretability and understanding for PyTorch
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
TorchDrift - Drift Detection for your PyTorch Models
model-validation-toolkit - Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.
pytea - PyTea: PyTorch Tensor shape error analyzer
feast - Feature Store for Machine Learning
cockpit - Cockpit: A Practical Debugging Tool for Training Deep Neural Networks
postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.
explainerdashboard - Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
giskard - 🐢 Open-Source Evaluation & Testing framework for LLMs and ML models
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both