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While playing with mpmpath and it’s Riemann Zeta function evaluator, I came upon those interesting animated plottings using Matplotlib (the source code is in the end of the post). Riemann zeta function is an analytic function and is defined over the complex plane with one complex variable denoted as ““. Riemann zeta is very important to mathematics due it’s deep relation with primes; the zeta func

JIT native code generation for TensorFlow computation graphs using Python and LLVM Update: Hacker News discussion here. The TensorFlow Computation Graph One of the most amazing components of the TensorFlow architecture is the computation graph that can be serialized using Protocol Buffers. This computation graph follows a well-defined format (click here for the proto files) and describes the compu

Deep learning – Convolutional neural networks and feature extraction with Python Convolutional neural networks (or ConvNets) are biologically-inspired variants of MLPs, they have different kinds of layers and each different layer works different than the usual MLP layers. If you are interested in learning more about ConvNets, a good course is the CS231n – Convolutional Neural Newtorks for Visual R

Google’s S2, geometry on the sphere, cells and Hilbert curve Update – 05 Dec 2017: Google just announced that it will be commited to the development of a new released version of the S2 library, amazing news, repository can be found here. Google’s S2 library is a real treasure, not only due to its capabilities for spatial indexing but also because it is a library that was released more than 4 years

After flying this past weekend (together with Gabriel and Leandro) with Gabriel’s drone (which is an handmade APM 2.6 based quadcopter) in our town (Porto Alegre, Brasil), I decided to implement a tracking for objects using OpenCV and Python and check how the results would be using simple and fast methods like Meanshift. The result was very impressive and I believe that there is plenty of room for

Machine Learning :: Text feature extraction (tf-idf) – Part II Read the first part of this tutorial: Text feature extraction (tf-idf) – Part I. This post is a continuation of the first part where we started to learn the theory and practice about text feature extraction and vector space model representation. I really recommend you to read the first part of the post series in order to follow this se

Machine Learning :: Text feature extraction (tf-idf) – Part I Short introduction to Vector Space Model (VSM) In information retrieval or text mining, the term frequency – inverse document frequency (also called tf-idf), is a well know method to evaluate how important is a word in a document. tf-idf are is a very interesting way to convert the textual representation of information into a Vector Spa

Genetic Programming and a LLVM JIT for restricted Python AST expressions A small intro on the rationale So I’m working on a Symbolic Regression Machine written in C/C++ called Shine, which is intended to be a JIT for Genetic Programming libraries (like Pyevolve for instance). The main rationale behind Shine is that we have today a lot of research on speeding Genetic Programming using GPUs (the GPU

I was taking a look at the proposal N2765 (user-defined literals) already implemented on the development snapshots of the GCC 4.7 and I was thinking in how user-defined literals can be used to create some interesting and sometimes strange constructions. Introduction to user-defined literals C++03 has some literals, like the “f” in “12.2f” that converts the double value to float. The problem is tha

Using pyearthquake to plot Japan USGS earthquake data into the near real-time MODIS satellite imagery The aim of this post is to show to the reader how to plot the recent Japan earthquake data from the USGS using the pyearthquake module. If you want to know more information about the pyearthquake module, take a look in this post where I previously used it. pyearthquake is a pure-python module whic

Here is two videos of a small script (python and xmlrpc calls to ubigraph visualization server) created to show a 3D graph of  the function call structure of a python application, the first shows only the structure created while running the application and the next video shows a debugging-like tool, it changes the node color to red when the function is called, and the labels shows: function name,