deepchecks
shapash
Our great sponsors
deepchecks | shapash | |
---|---|---|
15 | 8 | |
3,295 | 2,629 | |
2.7% | 2.4% | |
8.6 | 8.6 | |
6 days ago | 7 days ago | |
Python | Jupyter Notebook | |
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
-
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?
-
Data Validation tools
I use DeepChecks for my continuous training pipelines. You can check out the Data Integrity Checks.
-
How to trust your machine learning model with Deepchecks
Deepchecks (https://github.com/deepchecks/deepchecks) is an open-source Python package for comprehensively validating your machine learning models and data with minimal effort. This includes checks related to various types of issues, such as model performance, data integrity, distribution mismatches, and more.
Explore the docs https://docs.deepchecks.com
- [P] Deepchecks: an open-source tool for high standards validations for ML models and data.
shapash
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
-
This A.I.-generated artwork, Théùtre D'opéra Spatial, won first place at an art competition, and the art community isn't happy about it
There's work being done in that regard (like this python module), but as far as I know it's very clearly statistical guesstimates, and though it "works", the mathematical foundations are still somewhat shaky. There are heuristics in there we can't get rid of for now. But it's still better than nothing. Waaaaaay better than nothing.
-
Hacker News top posts: Jun 14, 2022
Shapash â Python library to make machine learning interpretable\ (4 comments)
-
State of the Art data drift libraries on Python?
Try out eurybia, from the author of shapash which is a brilliant library as well.
- [D] Has anyone ever used the SHAP and LIME models in machine learning?
What are some alternatives?
shap - A game theoretic approach to explain the output of any machine learning model.
great_expectations - Always know what to expect from your data.
interpret - Fit interpretable models. Explain blackbox machine learning.
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
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.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
feast - Feature Store for Machine Learning
trulens - Evaluation and Tracking for LLM Experiments
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.
GlassCode - This plugin allows you to make JetBrains IDEs to be fully transparent while keeping the code sharp and bright.
giskard - đą Evaluation & Testing framework for LLMs and ML models
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]