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apresentacoes

Apresentações sobre o Calango

awesome-profile-readme-templates

A collection of awesome readme templates to display on your profile

chiploadcalculator

A simple chip load calculator where you provide some needed parameters such as feedrate, number of flutes of the cutting bit, and spindle rotation and its returned the chip load value and a graph showing where this calculated value is in comparison with acceptable range for the current working material and bit diameter.

coloredbadges

Some badges I created for my GitHub profile readme.

fcastro25

freelancer_portfolio

This repository is destinated to future upwork clients with the purpose of gather all my personal python web scraping projects in one place.

gravmagsuite

GRAV MAG SUITE - A MATLAB-BASED SOFTWARE FOR PROCESSING POTENTIAL FIELD GEOPHYSICAL DATA

invg

A MATLAB GUI for forward modelling and compact inversion of gravity anomaly data

linear-regression-using-gradient-descent

This GUI was designed to aid college professors to teach how linear regression with gradient descent works in practice. *GUI features; This GUI enables the user to generate scattered points randomly with linear behavior and use the gradient descent algorithm to fit iteratively a line to the generated data. The process of generate the data can be done in two ways: by user clicking over the top graph, or automatically using the rand built in function of matlab. In the last way a buffer parameter can be set by the user to control how spread the points are. Increasing the value of this parameter will result the need to change the learning rates or the number of iterations as well, in order to fit the line properly. Once the user provided the data, a initial line can be generated whose angular and linear coeficients will be used as a starting point by the gradient descent algorithm. Parameters related to the gradient descent approach, like, number of iterations, and learning rates can be set by the user in order to perform the curve fitting efficiently. When the user click over the "optimize line" button the linear regression is done, and both loss function graphs and parameters table are updated/feeded in each iteration. Obs.: Be aware that providing small learning rate will slow the process of fitting with resonable loss value. And big learning rates will make gradient descent perform big jumps disabling it to converge to the loss function minima which will mess up the fitting process too.

mindcast

A React-Native streaming-audio app that provides knowledge in the shape of Podcasts.