This article will discuss and analyze the importance of k-fold cross-validation for model prediction in machine learning using the least-squares algorithm for Empirical Risk Minimization (ERM).
We’ll use a polynomial curve-fitting problem to predict the best polynomial for the sample dataset. Also, we’ll go over the implementation step-by-step of the 10-fold cross-validation on MATLAB.
By the end of this post, you’ll know how to implement the k-fold cross-validation method and understand the benefits and drawbacks that come with it.
To better visualize the benefits of applying k-fold cross-validation on machine learning, we’ll analyze some problems we may face when estimating…
This article will introduce key concepts about Regularized Loss Minimization (RLM) and Empirical Risk Minimization (ERM), and it’ll walk you through the implementation of the least-squares algorithm using MATLAB. The models obtained using RLM and ERM will then be compared and discussed against each other.
We’ll use a polynomial curve-fitting problem to predict the best polynomial for this data. The least-squares algorithm will be implemented step-by-step using MATLAB.
By the end of this post, you’ll understand the least-squares algorithm and be aware of the advantages and downsides of RLM and ERM. …
In this post, I’ll describe step-by-step how I built an entire distributed solution using only AWS and Azure free tier products. The services I created were scattered throughout both these cloud providers, and they are up and running in production mode 24/7 at this moment. These services were coded in Java and Angular. By the end of this post, you’ll know how to host your own applications in the cloud without spending a penny.
The solution I created is a software for calculating taxes and fees of financial transactions in the Brazilian stock exchange BM&FBOVESPA (B3).
Overall, this project is…
A few years ago, I’ve created a reliable and low-cost SNMP device to monitor and control a data center environment, and on this post, I’ll walk you through the entire design process I went through, as well as all features this network equipment is capable of. It’s safe to say this article ended up being quite long, so if something wasn’t clear enough, don't hesitate to contact me for further clarification.
This work resulted in the paper named Embedded Real-Time Environment Control System for Data Centers published in the XXIII CIENTEC 2017.
I organized this article into the following categories:
Nowadays, cloud solutions are gaining popularity day after day among giant companies that once relied on on-premise infrastructures and high-performance computer architectures, also known as mainframe-based systems. This trend was first boosted by big tech companies, especially the ones defined as FAAMG companies. With that mindset in mind, I’ve decided to create an article exploring some of the underlining benefits of event-based distributed systems using Kafka as the main message broker against classical monolithic applications and mainframe systems.
For this post, I’ve created a quick tutorial using two Spring Boot Java microservices, Docker, Schema Registry, and Kafka. …
You’re probably asking yourself, what a heck is Micronaut? Why not Spring Boot? If so, this is the post for you. You’re not alone! This is because these questions are often asked by many Spring programmers when first introduced to Micronaut.
With that in mind, I wrote this post to address some of the most common questions that may arise with the use of new frameworks like Micronaut. In this post, I’ll explain the major benefits and drawbacks of Micronaut when compared with Spring Boot, and I also walk you through your very first Kotlin microservice with the Micronaut Framework.
This is a quick tutorial on how to implement the Stochastic Gradient Descent (SGD) optimization method for SoftSVM on MATLAB to find a linear classifier with minimal empirical loss.
We’ll see how to tune our algorithm with different regularizers by analyzing the binary and hinge loss.
We will use a 4-dimensional dataset with 1,372 data points for this classification. This dataset is the banknote authentication dataset from the UCI repository. All source codes used in this tutorial are available in the repository below.
After downloading the data_banknote_authentication.txt file, we’ll import it on MATLAB in Home > Import Data. …
This post will dive deep into the concepts and theory behind hinge loss, logistic loss and binary loss for classification in machine learning. We’ll implement the perceptron algorithm on MATLAB, and see how we can select the best classifier based on the surrogate loss functions.
By the end of this post, you’ll know how to perform classification using the perceptron and the benefits and downsides of each loss function.
We’ll use a 3-dimensional dataset with N = 200 data points. This dataset was originally proposed by Dr. Ruth Urner on one of her assignments for a machine learning course. In…
Back in 2012, I and a couple of colleagues built a water-powered rocket for the final project in the fluid mechanics course of the Computer Engineering program at UFRN. This rocket was made of recycled PET bottles and some other easy-to-find materials like duct tape and garbage bags. Since this project was meant to teach us about mechanics and propulsion, I’ll include some of the math involved in the design of this prototype. I promised this won’t be a boring post, and it’ll be suitable for all ages.
The rocket frame was made by combining two used plastic bottles. The…
This is not clicking bate! I’ll show you how to put your Angular 8 APP in the cloud with Firebase for FREE forever in just under 2 minutes! If you think this is not a good deal, stick until the end of this article to find out how to get your free SSL and Domain to use with your application. This will be a short reading, I promise.
After all, what the heck is Firebase? Where this came from? Firebase is a company owned by Google which offers among other things mobile and app hosting. That’s all you need to…