perone

13 ms·

91.
Nanopipe: a C++ framework to connect message systems(nanopipe.readthedocs.io)
2 points by perone 10y ago|0 comments
92.
by perone 10y ago
The notebook of the presentation is here: https://github.com/perone/spark-als-intro/blob/master/spark-... If someone is interested.
93.
Introduction to large-scale recommendations using Apache Spark and Python(slideshare.net)
2 points by perone 10y ago|1 comments
94.
by perone 10y ago
This is actually easy to do, you just need to generate the IR and then merge them into a Module, after that you apply passes over the entire module to optimize, to do function inlining, etc.
95.
by perone 10y ago
Really nice project, congratulations ! I'll add a link in my post about it.
96.
by perone 10y ago
Yes it is, and in my opinion is very important. TensorFlow team is actually actively working on a JIT ( https://github.com/tensorflow/tensorflow/issues/164 ). I'll paste here a relevant part of the TensorF
97.
JIT native code generation for TensorFlow graphs using Python and LLVM(blog.christianperone.com)
95 points by perone 10y ago|19 comments
98.
by perone 11y ago
This is moving fast, a moratorium for some time would be beneficial for community. Like the one that happened in Python sometime ago.
99.
Protocoin v0.2 released (pure Python Bitcoin protocol parsing / p2p networking)(protocoin.readthedocs.org)
3 points by perone 11y ago|0 comments
100.
by perone 11y ago
Thanks for the feedback, I also believe that this approach has a lot of potential, especially after the work of Stephen Bax, a few word translations can help us to figure out transformations that could allow translation in vector space.
101.
by perone 11y ago
Hi, thanks for the feedback. There are three main points that makes me believe that the clusters aren't artificial: the first one is that I've made the clusters with DBSCAN on the original data (100-d word vectors) and not after t
102.
Voynich Manuscript: word vectors and t-SNE visualization of some patterns(blog.christianperone.com)
131 points by perone 11y ago|29 comments
103.
Voynich Manuscript: word vectors and t-SNE visualization of some patterns(blog.christianperone.com)
3 points by perone 11y ago|0 comments
104.
Convolutional hypercolumns in Python(blog.christianperone.com)
3 points by perone 11y ago|0 comments
105.
Deep Learning – Convolutional Neural Networks Presentation(slideshare.net)
3 points by perone 11y ago|0 comments
106.
by perone 11y ago
For those interested, there is also a course in Coursera by Hinton: https://class.coursera.org/neuralnets-2012-001
107.
Convolutional neural networks and feature extraction with Python(blog.christianperone.com)
82 points by perone 11y ago|2 comments
108.
Convolutional neural networks and feature extraction with Python(blog.christianperone.com)
1 points by perone 11y ago|0 comments
109.
by perone 11y ago
It isn't a matter of representation, but also how data structures could take advantage of such representations. B-trees for instance are very efficient with one-dimensional data. Take a look here: http://www.drdobbs.com&#x2F
110.
by perone 11y ago
I recommend you to read these two sources: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=731... http://www.fundza.com/algorithmic/space_filling/hilbert/basi...
111.
by perone 11y ago
Thanks. I solved the problem, lots of visits and missing swap space lol.
112.
Google’s S2, geometry on the sphere, cells and Hilbert curve(blog.christianperone.com)
166 points by perone 11y ago|29 comments
113.
Google’s S2, geometry on the sphere, cells and Hilbert curve(blog.christianperone.com)
7 points by perone 11y ago|0 comments
114.
Codex Seraphinianus deep learning dreams(pyevolve.sourceforge.net)
1 points by perone 11y ago|0 comments
115.
Luigi's Codex Seraphinianus deep learning dreams(blog.christianperone.com)
2 points by perone 11y ago|0 comments
116.
by perone 11y ago
Now I got it, thanks for the explanation Wes, sounds very interesting indeed. Congratulations for the project.
117.
by perone 11y ago
What are the main differences of this architecture when compared with the Apache Spark ? Something that I see as a nice advantage is the Python -> LLVM IR, but I can't see what are the main advantages over Spark.
118.
by perone 12y ago
In sum, everything you already have in Python.
119.
by perone 12y ago
One more reason that will make a lot of people to migrate to mbed platform.
120.
by perone 12y ago
As far as I understand, you can use open-source code for the monitor mode and also for the secure world of the TrustZone, so you're not required to use non-free software.

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