The following articles were authored by marcelojo

Logistic Regression – Hands on

Hello!

Today we’ll get hands dirty and test logistic regression algorithm. For this post, we are going to use the very known iris flower data set.This dataset has three classes of flowers which can be classified accordingly to its sepal width/length and petal width/length. From the dataset source “One class is linearly separable from the other 2 […]” which makes this dataset handy for our purposes of binary classification.

Continue reading Logistic Regression – Hands on

Logistic Regression

Hi folks,
Yeah, things are getting more interesting, huh? In the last posts we covered linear regression where we fit a straight line to represent the best way possible a set of points. This is a simple and powerful way to predict values. But sometimes instead of predicting a value, we want to classify them.

Why we would do that? I know that is easy to understand but for those who didn’t catch it, why this is interesting?

Continue reading Logistic Regression

Linear Regression with Normal Equation

Hello,

In this post we’ll show another way different to solve the problem of error minimization in linear regression. Instead of using gradient descent, we could solve this linear system using matrices.

Continue reading Linear Regression with Normal Equation

Gradient descent tricks

Hi people,

The last two posts were about linear regression. I explained a little about the theory and I left an example to test the algorithm which actually works but could be improved. How can we do this?

Continue reading Gradient descent tricks

Multiple Linear Regression

Hi folks,

In the last post we talked about simple linear regression, where we calculated the “trend line” of a set of points for a single variable. But what if we have more than a single variable? How can we solve it? Continue reading Multiple Linear Regression

Simple Linear Regression

Hi there,

After almost two years of personal hard work, I’m back to share a little about linear regression and gradient descent.

What is linear regression?

Continue reading Simple Linear Regression

Bootloader on STM32F0

Introduction

Besides all techniques created until today, every software developed can have bugs. Software we can always be updated to a new versions that fix all those bugs and it doesn’t take anything more than a few mouse clicks.  Embedded software ou bare bone firmware can have bugs too, but update to new versions is not always that simple as in computers software. Continue reading Bootloader on STM32F0

Camera Calibration – Part I

In the last few years we could notice that camera’s quality has been improved, its price has decreased at a point where we can find them almost everywhere. Most smart phones, if not all, have one or even two cameras. Youtube, Netflix, Flickr, Instagram are among the most popular websites around the world. At the same time, microprocessors became more powerful and cheaper in the way that programs can process image and video in real time.
Continue reading Camera Calibration – Part I

Histogram equalization

Hello,

In the last post I explained briefly what are histograms and today I’ll continue a little more and show how it can be useful to process images. Some examples of how histograms can be used are: image enhancement, texture classification, image segmentation, etc. In this post I’ll cover the most common use for histogram which is histogram equalization. Continue reading Histogram equalization

Image Histogram

Hello there,
Today I’ll cover a simple subject, image histogram. What is a histogram? Histogram is a graphical representation of a set of data separated in differents classes. It is represented by vertical bars where the base represents the class and the height represents the frequency/quantity of how many times it happened. Yes, it seems to be more complicated when we try to explain something really easy. Look at the figure 1 below Continue reading Image Histogram