Hacker News
- Interpretable Machine Learning https://christophm.github.io/interpretable-ml-book/ 5 comments
- Interpretable Machine Learning https://christophm.github.io/interpretable-ml-book/ 23 comments
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable https://christophm.github.io/interpretable-ml-book/ 10 comments
- Interpretable Machine Learning https://christophm.github.io/interpretable-ml-book/ 7 comments datascience
Linking pages
- Full article: What are the Most Important Statistical Ideas of the Past 50 Years? https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1938081 99 comments
- GitHub - learn-anything/books: Awesome Books https://github.com/learn-anything/books 6 comments
- eBooks | AnalytiXon http://advanceddataanalytics.net/ebooks/ 5 comments
- Nitpicking Machine Learning Technical Debt - matthewmcateer.me https://matthewmcateer.me/blog/machine-learning-technical-debt/ 3 comments
- free-programming-books/free-programming-books-subjects.md at main · EbookFoundation/free-programming-books · GitHub https://github.com/EbookFoundation/free-programming-books/blob/main/books/free-programming-books-subjects.md 3 comments
- Going beyond simple error analysis of ML systems https://alexandruburlacu.github.io/posts/2021-07-26-ml-error-analysis 2 comments
- GitHub - visenger/awesome-mlops: A curated list of references for MLOps https://github.com/visenger/awesome-mlops 2 comments
- Data Science 2020 - Highlights - by AbdulMajedRaja https://nulldata.substack.com/p/data-science-2020-highlights 1 comment
- Interesting Research Programs from the 2010s – Brett Mullins – Researcher - Data Scientist https://bcmullins.github.io/interesting-research-2010s/ 1 comment
- GitHub - rockita/criticalML: Toward ethical, transparent and fair AI/ML: a critical reading list for engineers, designers, and policy makers https://github.com/rockita/criticalML/blob/master/README.md 1 comment
- GitHub - erikgahner/awesome-statistics: A curated collection of links to statistics material https://github.com/erikgahner/awesome-statistics 0 comments
- GitHub - jphall663/awesome-machine-learning-interpretability: A curated list of awesome machine learning interpretability resources. https://github.com/jphall663/awesome-machine-learning-interpretability 0 comments
- Feature importance — what’s in a name? | by Sven Stringer | bigdatarepublic | Medium https://medium.com/bigdatarepublic/feature-importance-whats-in-a-name-79532e59eea3 0 comments
- How to Mitigate Bias in AI Systems | Toptal https://www.toptal.com/artificial-intelligence/mitigating-ai-bias 0 comments
- GitHub - endymecy/awesome-deeplearning-resources: Deep Learning and deep reinforcement learning research papers and some codes https://github.com/endymecy/awesome-deeplearning-resources 0 comments
- Challenges in Few-shot learning; 2019 predictions; JAX; Explainable models; MT reading list; Foundations of ML; AI Index 2018; Karen Sparck Jones; Analysis methods survey; ICLR 2019 rejects | Revue http://newsletter.ruder.io/issues/challenges-in-few-shot-learning-2019-predictions-jax-explainable-models-mt-reading-list-foundations-of-ml-ai-index-2018-karen-sparck-jones-analysis-methods-survey-iclr-2019-rejects-151442 0 comments
- GitHub - csinva/imodels: Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). https://github.com/csinva/imodels 0 comments
- Interpretable Machine Learning with Python - Savvas Tjortjoglou http://savvastjortjoglou.com/intrepretable-machine-learning-nfl-combine.html 0 comments
- GitHub - csinva/imodels: Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). https://github.com/csinva/interpretability-implementations-demos 0 comments
- Understanding Black-Box ML Models with Explainable AI | by Florian Perteneder | Dynatrace Engineering | Medium https://medium.com/dynatrace-engineering/understanding-black-box-ml-models-with-explainable-ai-40626cc39520 0 comments
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