djgagne / ams-ml-python-course Goto Github PK

Hello David,

I am Chiara Lepore, a Research Scientist at LDEO-Columbia University. We briefly met this summer over lunch at the Hail Workshop held in Boulder.
I have seen your tweet about your repo with the ML classes, and I am excited to give it a try.

I am contacting you, right now, to introduce you to a great open source effort brought forward by a large community, called Pangeo. Since you have shared your course (thank you!) I gather you are keen to share your efforts in an open source community, and I think you will find Pangeo very interesting and helpful.

Pangeo is a NSF EarthCube funded project and it is, as of now (it has changed scope in the past year), very much interested in allowing simple Big Data analysis in the cloud, with a specific focus on Geosciences. You can read more about it here on its website and check out the GitHub repo where we use the issue section to communicate and discuss further developments.

I thought about introducing you to Pangeo when I have looked at the section in which you carefully describe how to set up the Jupyter Hub Containers.
In fact, one of the main goal of Pangeo, is to develop cloud computing platforms that are already set up for people, like me for example, who could find complicated to set it up on their own. More over, Pangeo is right now - although it will soon be dismissed - providing a cloud computing platform open to everyone (after they request access through an issue on the repo) to try out Jupyter on the cloud.

Yesterday a new issue was opened in which folks interested in ML are going to discuss ways Pangeo can help simplify the workflow and provide support to people interested in ML. In fact, one of the goals of Pangeo, being a NSF funded proposal, is also to provide open source tutorials, workflows, and anything that can help people spun up and over the initial hump that sometimes setting up environments, dealing with large data, preprocessing etc, can create.

I am not the best person to describe the technical details of Pangeo infrastructure, but I hope you will join us for a chat and that Pangeo can help you with your ML course.
One specific thing we could try is to turn your course into a binder that runs on the pangeo binder service.

I would like to make the following changes to the short course before the SEA Conference:

Overall

• Add an interactive example for each module.
• Move low-level code out of notebooks to the library.

Module 1 (DJ):

• Fix PCA example and add visualization of PCA.

Module 2 (Ryan):

• Move some of the material, especially training/test set split to module 1.
• DJ will take the pre-processing items out of module 2 and then Ryan will finish other edits.

Module 3 (DJ):

Add headings.
Do actual backwards optimization on 32 subplot image.
Potentially switch out the ridge and lasso plot with https://stats.stackexchange.com/questions/341816/why-the-contour-of-lasso-and-ridge-regression-are-drawn-only-at-that-position-an.

Module 4 (Ryan):

Remove pre-processing and training.
Redo ordering:

1. Permutation Variable Importance
2. Saliency
3. Grad-CAM
4. Backwards Optimization
   Bonus: Novelty Detection.

Ryan's Notes

Will be 6 hours (0900-1600 with one-hour lunch break).
We plan to cover the same topics roughly.
Just Ryan and DJ teaching.

I will put in Grad-CAM before novelty detection and leave novelty detection as a bonus topic.
Replace “Citation” with actual title of my notebook.
Remove the long list of “pip install”.
Put constants in dictionary?
Don’t cover plotting and norm/denorm.
I should just have one block that loads all the data, normalizes, and binarizes.
I also will focus less on CNN setup.
Do motivation for interpretation at the beginning.
I can probably include more details on evaluation.
Olah figure with all the different chunking options might be confusing, since we use only output neuron.
DJ has a flow chart that explains BWO (I will use this).
Put saliency before BWO.
Order: saliency, Grad-CAM, BWO, novelty detection.

I ran into a few quirks installing this on Windows (7 x64). First, an existing installation of python through Visual Studio was causing all kinds of problems, had to completely remove everything python and reinstall miniconda.

I also needed to run the anaconda command prompt as administrator for certain packages to install properly.

Finally, there were a few packages missing after walking through your setup instructions (local install). I had to add the following through conda install:

geos
cartopy
jupyterlab

Now it seems to be working.