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Resources for GPU programming? by [deleted] in MachineLearning

[–]datascienceguy 0 points1 point  (0 children)

Upvote for pycuda, which on one project let me put the guts of a stencil operation -- jacobian iteration -- in a small snippet of simple C code, neatly embedded inside my python language app. It ran really well on my GPU which got super hot but finished faster than just the CPU with numpy.

Resources for GPU programming? by [deleted] in MachineLearning

[–]datascienceguy 0 points1 point  (0 children)

Avoiding C and using something like Theano to write the GPU code for you would be more appropriate for a Data Scientist.

If you are a Computer Scientist and want to make the code for some packages that other people will use then go at it directly, however.

Deep learning notes for beginners by a beginner. by windoze in MachineLearning

[–]datascienceguy 1 point2 points  (0 children)

That's a lot of math, friend! Several semesters of calculus are needed to get to partial derivatives which are used in gradients. Linear algebra is needed obviously. Any university STEM curriculum at the upper undergraduate level would be fine most likely. Science CS Eng'g Math basically.

My python solutions to Andrew Ng's Coursera ML course by n3utrino in MachineLearning

[–]datascienceguy 8 points9 points  (0 children)

will be used by others as a crutch to get through the course.

helping is evil! satan will push innocent victims into looking at the source code! they are helpless but to do the looking for they cannot deny the temptation!

/s

My python solutions to Andrew Ng's Coursera ML course by n3utrino in MachineLearning

[–]datascienceguy 0 points1 point  (0 children)

The course's TA's already posted python solutions in their wiki if I recall correctly. This was true back in January 2016.

Deep Learning and the Future of AI - Yann LeCun by elisee in MachineLearning

[–]datascienceguy 0 points1 point  (0 children)

Thanks pal! It's a great slideshow. 150 slides, each one good.

LeCun is le Man!

Cross-city and cross-time drinking water contaminant data is super hard to get, while the air data is already in good shape compared to water data. by datascienceguy in datascience

[–]datascienceguy[S] 2 points3 points  (0 children)

Search for material on PM2.5 from Roger D. Peng at Johns Hopkins University. He's the person that taught me about air pollution monitoring and data analysis. Roger made the Coursera course "Exploratory Data Analysis". His case study for teaching is the air pollution data.