Blog: Machine Learning

In this article, you’ll learn about what prompt engineering is, how it works, and even learn a couple of tricks that will help you work with generative AI systems like a pro.

In this blog post, we’ll explore the foundational concepts of the F1 score, discuss its limitations, and look at use cases across diverse domains.

As AI systems become more prevalent in businesses and homes, the need for robust security measures to protect these systems has become increasingly important. In this article, we look at AI security and why it's important.

TinyML – machine learning in embedded devices – is actively gaining popularity. This post will explain how TinyML works and why it is important.

In this blog post, we dive inside СhatGPT’s brain and explore large language models (LLMs) and other technologies that allow bots to communicate like people.

AI alignment is a new field that studies how to make it so that the created systems would answer the needs and expectations of humans. In this post, we share insights into what AI alignment is and how it works.

From political propaganda to fraudulent scams, deepfakes can be used to create convincing but entirely fabricated depictions of events that never happened.

Word2vec, short for "word to vector," is a technology used to represent the relationships between different words in the form of a graph. This technology is widely used in machine learning for embedding and text analysis.

In this article, we give a comprehensive overview of AI project ideas to work on, from beginner-friendly tasks to more advanced challenges.

In this article, we look at the technology behind GPT-3 and GPT-4 – transformers. We’ll talk about what transformers are, how they work, and why they are so important for technology and business.

In this interview, Jesse Johnson – a leading data science expert and the founder of the Merelogic consulting group – shares his thoughts about the challenges of AI analysis in biological research.

The relationship between bias and variance is similar to overfitting and underfitting in machine learning. Learn how to achieve optimal model performance by keeping in mind the bias-variance tradeoff.