Dev

Imagine que você está na maior biblioteca do mundo. Há milhões de livros ao seu redor. Agora, alguém te pede para encontrar um livro específico.

Se você já fez um TCC, dissertação ou tese, sabe que a reta final é muitas vezes ofuscada por um monstro burocrático: a formatação nas normas da ABNT. Ajustar margens, espaçamentos, citações e referências em um arquivo .docx caótico não é apenas entediante, é um verdadeiro ralo de produtividade e paz mental.

If you’ve ever wondered how a computer can predict whether an email is spam or not, whether a customer will buy a product, or whether a patient has a disease, then you’ve already brushed against the power of Logistic Regression.

Welcome back to our blog, where we dive into the fascinating world of computer vision! Today, we are going to explore a fundamental concept in image processing: operations in spatial and frequency domains. 🖥️🔍

In the vast landscape of Machine Learning, some algorithms stand out for their intuitive nature and powerful performance. Decision-Based Learning centers around models that make decisions by following a series of rules, much like how humans make choices step by step. This approach often leads to models that are easy to understand and interpret.

Deep Learning is a powerful subset of Machine Learning that draws inspiration from the human brain’s structure and function, particularly through artificial neural networks. Instead of explicit programming, deep learning models learn intricate patterns and representations directly from raw data. This remarkable ability allows them to excel in complex tasks like image recognition and natural language processing.

Machine Learning (ML) is a fascinating field where computers learn from data without being explicitly programmed. Instead of writing rigid rules for every possible scenario, we feed algorithms data, and they figure out patterns, make predictions, or take actions based on what they’ve “learned.” It’s like teaching a child by showing them examples rather than giving them a rulebook.

If you’re diving into the world of machine learning with Python, you’ll quickly come across scikit-learn. It’s an open-source library that provides a wide range of efficient tools for various machine learning tasks. Think of scikit-learn as a Swiss Army knife for data scientists – it has almost everything you need for common machine learning workflows, all neatly organized and easy to use.

In the rapidly evolving world of artificial intelligence, deep learning stands out as a transformative technology. At its core, deep learning relies on frameworks that allow developers and researchers to build, train, and deploy complex neural networks. Two of the most prominent players in this arena are TensorFlow and PyTorch.

Life is full of uncertainties, from whether it will rain tomorrow to the chances of your favorite sports team winning their next game. Probability is the language we use to quantify these uncertainties. It provides a framework for understanding the likelihood of different events occurring. Think of it as a way to put a number on how likely something is to happen. A probability of 0 means an event is impossible, while a probability of 1 means it’s certain. Anything in between represents varying degrees of likelihood.

Ever wondered how to make sense of a jumble of numbers? That’s where descriptive statistics come in! Think of descriptive statistics as your data’s personal storyteller. It helps you summarize, organize, and simplify complex datasets without delving into complex conclusions or predictions. It’s the first crucial step in any data analysis journey, giving you a clear picture of what you’re working with.

Have you ever wondered who actually builds the artificial intelligence tools we use every day? Not the scientist who invents the algorithm in a lab, but the person who connects that “magic” to real products like Netflix, Google, or GitHub Copilot.

Functional programming in Java in a better way.

Pandas é uma biblioteca open source, licenciada pelo BSD, que fornece estruturas de dados de alto desempenho, fáceis de usar e ferramentas de análise de dados para a linguagem de programação Python.

A carreira de Cientista de Dados é frequentemente citada como uma das mais promissoras do século. Mas como dar os primeiros passos nesse universo, especialmente utilizando Python?

Python has several artithmetic operators: +, -, *, /, %, **, //.

console.xxx is a great way to debug the code. I know that we could use a debug tool, but I prefer console. So let’s see some useful hint about it.

Array instances inherit from Array.prototype. As with all constructors, you can change the constructor’s prototype object to make changes to all Array instances. For example, you can add new methods and properties to extend all Array objects. This is used for polyfilling, for example.

If the first line of a script file starts with #!, it is known as a “shebang” line. Linux and other Unix like operating systems have native support for such lines and they are commonly used on such systems to indicate how a script should be executed. This launcher allows the same facilities to be used with Python scripts on Windows.

O target='_blank' é muito usado quando queremos abrir um link em outra aba. Apenas não o use sozinho por questões de segurança.

Quem nunca usou o console.log para depurar um código e ver o valor daquela variável ou até mesmo para checar se uma função foi chamada devidamente? 🙈

Como publicar seu app JS no Firebase usando a ferramenta de Integração Contínua Travis CI

O GitHub é uma ótima ferramenta não só para repositório de código mas também para publicar nossos apps/websites. Veja como é fácil publicar lá.

O Firebase é uma plataforma para desenvolvimento mobile e web do Google. Com ela, você pode desenvolver apps de qualidade rapidamente. Neste post, vamos focar na publicação gratuita.