perone

7 ms·

31.
Tutorial on using LLVM to JIT PyTorch graphs to native code (x86/arm/RISC-V)(blog.christianperone.com)
4 points by perone 4y ago|0 comments
32.
Arduino WAN, Helium network and cryptographic co-processor(blog.christianperone.com)
1 points by perone 5y ago|0 comments
33.
by perone 5y ago
It is actually used a lot in biomedical domain, however the gains a minimal, quite different in practice than what you see in papers.
34.
by perone 5y ago
As someone who worked with these techniques a lot in the past, I can say that SSL definitely makes sense in theory, but in practice, the gain doesn't pay off the complexity, except in rare cases w/ pseudo-labelling for example, wh
35.
by perone 5y ago
Actually it would be much better if they just invested in VSCode for that.
36.
Show HN: Episuite, open-source framework for epidemiology in Python(perone.github.io)
12 points by perone 6y ago|0 comments
37.
Introduction to gradient-based optimization in Deep Learning [slides](drive.google.com)
1 points by perone 6y ago|0 comments
38.
Slides: Gradient-based optimization in Deep Learning(drive.google.com)
3 points by perone 6y ago|0 comments
39.
A new professional ethics: Karl Popper and Xenophanes’ epistemology(blog.christianperone.com)
3 points by perone 6y ago|0 comments
40.
A sane introduction to maximum likelihood (MLE) and maximum a posteriori (MAP)(blog.christianperone.com)
1 points by perone 6y ago|0 comments
41.
Gandiva, Using LLVM and Arrow to JIT and Evaluate Pandas Expressions(blog.christianperone.com)
1 points by perone 7y ago|0 comments
42.
Show HN: Gandiva, Using LLVM and Arrow to JIT and Evaluate Pandas Expressions(blog.christianperone.com)
2 points by perone 7y ago|0 comments
43.
A sane introduction to maximum likelihood estimation and MAP(blog.christianperone.com)
5 points by perone 7y ago|0 comments
44.
EuclideDB: Machine learning feature database tight coupled with PyTorch(euclidesdb.readthedocs.io)
15 points by perone 7y ago|0 comments
45.
Uncertainty Estimation in Deep Learning(slideshare.net)
1 points by perone 7y ago|0 comments
46.
by perone 7y ago
The only issue is that you won't be able to find reasonable libraries for it, all of them are just PoCs without testing or stability.
47.
Uncertainty Estimation in Deep Learning [slides](slideshare.net)
1 points by perone 7y ago|0 comments
48.
Uncertainty Estimation in Deep Learning [slides](slideshare.net)
1 points by perone 7y ago|0 comments
49.
Numpy dispatcher: when Numpy becomes a protocol for an ecosystem(blog.christianperone.com)
1 points by perone 7y ago|0 comments
50.
Numpy dispatcher: when Numpy becomes a protocol for an ecosystem(blog.christianperone.com)
2 points by perone 7y ago|0 comments
51.
PyTorch Under the Hood [slides](speakerdeck.com)
1 points by perone 7y ago|0 comments
52.
by perone 8y ago
That is an important point, Google is the master of releasing things in half, this was a common practice in Tensorflow since the initial release, they basically removed a lot of things to release it and it became a Frankenstein base of code
53.
by perone 8y ago
Is there any link with the talk ?
54.
PyTorch Under the Hood(speakerdeck.com)
3 points by perone 8y ago|0 comments
55.
PyTorch under the hood (slides from PyData Montreal)(speakerdeck.com)
2 points by perone 8y ago|0 comments
56.
EuclidesDB v.0.2.0 released, with Faiss support and libtorch 1.0.1(euclidesdb.readthedocs.io)
1 points by perone 8y ago|0 comments
57.
by perone 8y ago
For that, I would need the likelihood of all others xD
58.
by perone 8y ago
Will fix it, thanks a lot for the feedback !
59.
A sane introduction to maximum likelihood estimation and maximum a posteriori(blog.christianperone.com)
174 points by perone 8y ago|18 comments
60.
by perone 8y ago
This is for MRI reconstruction, it has no other labels or annotations, as far as I know, only the raw data in k-space and the reconstruction. It's also only for knee.

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