Training machine learning models requires a lot of data, which is not always available. This is where transfer learning comes into play as it leverages previously trained models. In this blog post, we’ll explore the definition, methodology, benefits, and applications of transfer learning. …

The support vector machine (SVM) algorithm is a machine learning algorithm widely used because of its high performance, flexibility, and efficiency. In most cases, you can use it on terabytes of data, and it will still be much faster and cheaper than working with deep neural networks. The algorithm is…

The most popular solutions for storing data today are data warehouses, data lakes, and data lakehouses. This post gives a detailed overview of these storage options and their pros and cons for specific purposes. What is a data warehouse? A data warehouse (often abbreviated as DWH or DW) is a structured repository of data collected…

The k-nearest neighbors (kNN) algorithm is a simple tool that can be used for a number of real-world problems in finance, healthcare, recommendation systems, and much more. This blog post will cover what kNN is, how it works, and how to implement it in machine learning projects. What is the k-nearest neighbors classifier? The k-nearest neighbors…

Passport checks at airports, automatic turnstiles in office centers, signature verification — all these tasks require face or object recognition. Historically, deep learning algorithms use a lot of labeled training data for simple tasks like identifying objects in photos or recognizing faces. But in the above situations, we normally do…

Deep learning has been a game changer in the field of computer vision. It’s widely used to teach computers to “see” and analyze the environment similarly to the way humans do. Its applications include self-driven cars, robotics, data analysis, and much more. In this blog post, we will explain in…

What if you could predict a market crash or a stock price fall? How about detecting an earthquake before it happens? What potential does AI have for diagnosing serious health conditions like cancer? Pattern recognition — finding hidden patterns in data — is one way to effectively solve problems and…

In discussions about Haskell, sometimes a question is raised — what exactly can you build with the language? To answer this question, this article will look at some of the more interesting Haskell open-source projects on GitHub. Among them, you will find cool user-facing applications, such as SimpleX and hledger…

Predicting customer behavior, consumer demand or stock price fluctuations, identifying fraud, and diagnosing patients — these are some of the popular applications of the random forest (RF) algorithm. Used for classification and regression tasks, it can significantly enhance the efficiency of business processes and scientific research. This blog post will…