README
[[http://ml.nau.edu][Machine learning research lab]] group meetings - [[file:semester-2024-01/README.org][Spring 2024]] - [[file:semester-2023-08/README.org][Fall 2023]] - [[file:semester-2023-01/README.org][Spring 2023]] - [[file:semester-2022-08/README.org][Fall 2022]] - [[file:semester-2022-01/README.org][Spring 2022]] - [[file:semester-2021-08/README.org][Fall 2021]] - [[file:semester-2021-01/README.org][Spring 2021]] - [[file:semester-2020-08/README.org][Fall 2020]] - [[file:semester-2020-01/README.org][Spring 2020]] - [[file:semester-2019-08/README.org][Fall 2019]] Advice on preparing talks/slides - https://emilyriederer.netlify.app/post/writing-a-tech-talk/ Ideas for talks - [[https://www.youtube.com/watch?v=GUovhZYNO-M][Don't use VSCode]] - https://github.com/mlabonne/llm-course - https://github.com/microsoft/generative-ai-for-beginners - https://github.com/microsoft/ML-For-Beginners - When there are new people in lab at the beginning of a semester, tutorial about - git - data.table - ggplot2 - Grad student reading group [[https://class.lambdamd.org/pdsr/][Programming for Data Science in R]] - Z. Yang, Q. Xu, S. Bao, X. Cao, and Q. Huang. Learning with multiclass auc: Theory and algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. - https://developers.google.com/machine-learning/guides/rules-of-ml - anything from the Murphy book, https://github.com/probml/pml-book/releases/latest/download/book1.pdf - Causality for Machine Learning https://arxiv.org/abs/1911.10500 - Efficient and Modular Implicit Differentiation, https://arxiv.org/abs/2105.15183 - Hyperparameter optimization with approximate gradient, https://arxiv.org/pdf/1602.02355.pdf - Deep Implicit Layers Tutorial at NeurIPS 2020, http://implicit-layers-tutorial.org/ - anything from the Sussex PAL reading group, https://wearepal.ai/reading - Intro to jax in Python, https://jax.readthedocs.io/en/latest/notebooks/quickstart.html - Tutorial how to use Tensor Processing Units (TPUs), https://cloud.google.com/tpu/docs/tpus - Jenny Bryan on R debugging and minimal reproducible examples, https://www.youtube.com/watch?v=vgYS-F8opgE - https://github.com/ReeceGoding/Frustration-One-Year-With-R - Changepoint review paper https://arxiv.org/pdf/2012.12814.pdf - https://github.com/matloff/TidyverseSkeptic/blob/master/READMEFull.md
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README
[[http://ml.nau.edu][Machine learning research lab]] group meetings - [[file:semester-2024-01/README.org][Spring 2024]] - [[file:semester-2023-08/README.org][Fall 2023]] - [[file:semester-2023-01/README.org][Spring 2023]] - [[file:semester-2022-08/README.org][Fall 2022]] - [[file:semester-2022-01/README.org][Spring 2022]] - [[file:semester-2021-08/README.org][Fall 2021]] - [[file:semester-2021-01/README.org][Spring 2021]] - [[file:semester-2020-08/README.org][Fall 2020]] - [[file:semester-2020-01/README.org][Spring 2020]] - [[file:semester-2019-08/README.org][Fall 2019]] Advice on preparing talks/slides - https://emilyriederer.netlify.app/post/writing-a-tech-talk/ Ideas for talks - [[https://www.youtube.com/watch?v=GUovhZYNO-M][Don't use VSCode]] - https://github.com/mlabonne/llm-course - https://github.com/microsoft/generative-ai-for-beginners - https://github.com/microsoft/ML-For-Beginners - When there are new people in lab at the beginning of a semester, tutorial about - git - data.table - ggplot2 - Grad student reading group [[https://class.lambdamd.org/pdsr/][Programming for Data Science in R]] - Z. Yang, Q. Xu, S. Bao, X. Cao, and Q. Huang. Learning with multiclass auc: Theory and algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. - https://developers.google.com/machine-learning/guides/rules-of-ml - anything from the Murphy book, https://github.com/probml/pml-book/releases/latest/download/book1.pdf - Causality for Machine Learning https://arxiv.org/abs/1911.10500 - Efficient and Modular Implicit Differentiation, https://arxiv.org/abs/2105.15183 - Hyperparameter optimization with approximate gradient, https://arxiv.org/pdf/1602.02355.pdf - Deep Implicit Layers Tutorial at NeurIPS 2020, http://implicit-layers-tutorial.org/ - anything from the Sussex PAL reading group, https://wearepal.ai/reading - Intro to jax in Python, https://jax.readthedocs.io/en/latest/notebooks/quickstart.html - Tutorial how to use Tensor Processing Units (TPUs), https://cloud.google.com/tpu/docs/tpus - Jenny Bryan on R debugging and minimal reproducible examples, https://www.youtube.com/watch?v=vgYS-F8opgE - https://github.com/ReeceGoding/Frustration-One-Year-With-R - Changepoint review paper https://arxiv.org/pdf/2012.12814.pdf - https://github.com/matloff/TidyverseSkeptic/blob/master/READMEFull.md
Not written in Markdown, so it's shown here as plain text β view it formatted on GitHub.