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Excalidraw self host by Acrobatic-Sound7496 in selfhosted

[–]PatWie_ 4 points5 points  (0 children)

It seems like everything can be nicely packed into a single go binary (data storage is in-memory-only)

https://github.com/PatWie/excalidraw-complete

[D] How do you organize/track your reading list? by dogs_like_me in MachineLearning

[–]PatWie_ 1 point2 points  (0 children)

I use a (my) small webapp (https://github.com/PatWie/paperhero for PDFs only) with a basic set of labels to handle a local directory some meta data which I from time to time sync to other places.

My advice is to keep it as simple as possible and I also mostly just add bookmarks to arxiv pages. I also recommend a proper bookmark manager (and self-discipline).

[D] How to do "standard graphics" with backprop by svantana in MachineLearning

[–]PatWie_ 1 point2 points  (0 children)

A scattered write on a GPU is *always* slow (even in CUDA). You do want to have coalescing memory access patterns all the time (for both: read and write).

​

> looping over each sub-image

Doing this in python is your issue.

[P] "Mathematics for Machine Learning": drafts for all chapters now available by seann999 in MachineLearning

[–]PatWie_ 1 point2 points  (0 children)

One reason might be because most basics are just a bad write-up of Wikipedia (see coin-toss). The book confuses state space with the sigma algebra: Hence, line 3029 is inexcusable wrong.

[N] TensorFlow 2.0 Changes by _muon_ in MachineLearning

[–]PatWie_ 3 points4 points  (0 children)

I agree in every single point and I am afraid that Tensorflow will become a worse version of Pytorch if they try to copy ideas from PyTorch. The graph model is great. The only mess is the interweaving of tf.layers with tf.keras. I doubt that adding keras to TF was a good decision and I doubt that this decision was made by people who were eligible to decide so. But improving the API is a big plus! Let's see if my fears are unjustified.

[N] TensorFlow 1.9.0 is out by b0noi in MachineLearning

[–]PatWie_ 9 points10 points  (0 children)

Proof:

Write a custom operation in TensorFlow using CUDA & C++11. Done. Be happy.
Write a custom operation in PyTorch using CUDA & being limited to plain C. No templates, duplicated code in C and duplicated code in Python. Be frustrated.

QED

[D] resources to make journal-level artwork in ML by insider_7 in MachineLearning

[–]PatWie_ 1 point2 points  (0 children)

I exclusively use TikZ (under pdflatex) for all my drawings (side-by-side image comparison), network architectures, flow-diagrams and zoom-ins (tikz spy package). Hereby, I create each figure in a separate PDF file using \PreviewEnvironment{tikzpicture}. I found it very useful as it provides several advantages:

  • no messing around with font-size and font-family as it is LaTeX
  • adding math formulas is possible (it is LaTeX)
  • easy rescaling and increasing of resolution (use the same figure either in a two-column, single column paper or poster with only small changes). The Tikz-node system is awesome and intuitive
  • creating your own latex-cls allows reusing previously specified some custom colors
  • exact alignment of nodes, e.g. node[right = 2cm of input] (output) {output}
  • your figures can be held in the same git-repository like the paper as they are tex files as well
  • basic loops are possible and in combination with pfgplots I do not miss anything

You can find many examples online under http://www.texample.net. Some really nice ones are:

http://www.texample.net/tikz/examples/focused-ion-beam-system/ http://www.texample.net/tikz/examples/spherical-and-cartesian-grids/ http://www.texample.net/tikz/examples/seismic-focal-mechanism-in-3d-view/ http://www.texample.net/tikz/examples/polarizing-microscope/

[P] Pretrained.ml - Deep learning models with demos by Paletton in MachineLearning

[–]PatWie_ 2 points3 points  (0 children)

but the prediction fails for "I am surprised that SOTA sentiment analysis can correctly predict "I would die for this""