New paper: A Sampling Algorithm for Diffusion Networks
Conference paper presented on the 28th European Signal Processing Conference (EUSIPCO) in which we propose an adaptive sampling method for the diffusion networks.
New paper: A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks
This paper summarizes the results obtained by Daniel G. Tiglea during the period he was working to obtain the M.S. Degree.
7 Python Code Examples for Everyday Use
On September 27, 2020 the article 7 Python Code Examples for Everyday Use was published on Go Skills. In the following, you’ll find the summary and the link to the code on Github.
Generative Adversarial Networks: Build Your First Models
How To Set Up a PageKite Front-End Server on Debian 9
On October 25, 2019 the article How To Set Up a PageKite Front-End Server on Debian 9 was published on Digital Ocean. In the following, you’ll find the summary and the link to the article on the Digital Ocean website.
Arduino With Python: How to Get Started
On October 21, 2019 the article Arduino With Python: How to Get Started was published on Real Python. In the following, you’ll find the summary and the link to the article on the Real Python website.
A Brief Introduction to GANs – SciPy Meetup Talk
On the 15th of August, I presented a talk on SciPy Meetup – Coders Hub Powered by Giant Steps. Here are the summary of the presentation, slides and code (in portuguese).
New paper: An Adaptive Sampling Technique for Graph Diffusion LMS Algorithm
Conference paper presented on the 27th European Signal Processing Conference (EUSIPCO) in which we propose an adaptive sampling method for the diffusion algorithm for adaptively learning from streaming graphs signals.
Setting Up Python for Machine Learning on Windows
This Post Was Originally Published on Real Python on Oct 31st, 2018 by Renato Candido.
Python has been largely used for numerical and scientific applications in the last years. However, to perform numerical computations in an efficient manner, Python relies on external libraries, sometimes implemented in other languages, such as the NumPy library, which is partly implemented using the Fortran language.
Due to these dependencies, sometimes it isn’t trivial to set up an environment for numerical computations, linking all the necessary libraries. It’s common for people to struggle to get things working in workshops involving the use of Python for machine learning, especially when they are using an operating system that lacks a package management system, such as Windows.
In this article, you’ll:
- Walk through the details for setting up a Python environment for numerical computations on a Windows operating system
- Be introduced to Anaconda, a Python distribution proposed to circumvent these setup problems
- See how to install the distribution on a Windows machine and use its tools to manage packages and environments
- Use the installed Python stack to build a neural network and train it to solve a classic classification problem