Anthony Gitter

👋 that was me. The workshop was great. I've shared the materials with others in my research group who couldn't attend. I recall that `another-greeting` example [here](https://github.com/dme26/docker-introduction/blob/6a6e097536a9b5c5ae62979f2f0207a43e3f8562/_episodes/05-creating-container-images.md) does intentionally fail...

> Further is the possibility to demonstrate the value of merging consecutive RUN lines in terms of reducing the number of layers. That's a good idea. That structure confused me...

Amazon's Machine Learning University is now [open](https://www.amazon.science/latest-news/machine-learning-course-free-online-from-amazon-machine-learning-university) to the public.

[Machine learning resources](https://e2eml.school/machine_learning_resources.html) list

[Machine learning in Python with scikit-learn](https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/) is an online course from the scikit-learn team. Their modules follow some of our content but presumably focus on coding these things in scikit-learn.

Kaggle has Python-based [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) lessons and exercises

[How to avoid machine learning pitfalls: a guide for academic researchers](https://arxiv.org/abs/2108.02497)

mikropml is an R package for supervised learning pipelines: [paper](https://doi.org/10.21105/joss.03073), [CRAN package](https://cran.r-project.org/package=mikropml) It builds on caret.

[Navigating the pitfalls of applying machine learning in genomics](https://doi.org/10.1038/s41576-021-00434-9)

Carpentries [introduction to deep learning](https://github.com/carpentries-incubator/deep-learning-intro/).