Multimodal learning is a machine learning technique that incorporates data from multiple modalities – ways of perceiving the world – to create models that have increased accuracy and better capabilities.
K-means is an algorithm that can separate unlabeled data into a predetermined number of clusters. In this blog post, we look at its underlying principles, use cases, as well as benefits and limitations.
In this interview, we speak with Dr. Yuriy Gankin and Maxim Kazanskii from Quantori – a data science and digital transformation company specializing in research and development in the biopharma industry.
Stable Diffusion is a free, open-source neural network for generating photorealistic and artistic images based on text-to-image and image-to-image diffusion models. Read our tips to start generating your own masterpieces in minutes.
In this blog post, we explore the definition, methodology, benefits, and applications of transfer learning. We also discuss various transfer learning strategies and provide a selection of pre-trained models.
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.
The k-nearest neighbors (kNN) algorithm is a simple tool that can be used for a large number of real-world problems. In this article, we cover what kNN is, how it works, and how to implement it in machine learning projects.