Introduction RAG stands for “retrieval augmented generation”. RAG is intended to allow a “large language model” (LLM) to use additional information provided by a user. LLMs are trained on large amounts of text, usually obtained from the Internet. However, it…
Introduction Modeling and explaining the consumer behavior of a product is very important to know what factors affect their behavior, price being one of these important factors. Let’s take as an example a company X that sells clothing. It is…
Introduction A linear regression is a model that is used to approximate the linear relationship between a dependent variable Y and a set of independent variables X. In matrix format it can be expressed as where ε is a vector…
Introduction All graphics in this article are interactive due to the nature of trying to represent text graphically. To see which sentence each point represents, simply hover the mouse over it. The results obtained have been generated on the “STS…
What is a digital twin? When we talk about digital twins, we are mainly referring to a virtual representation of a physical system, where a copy (twin) of the real environment is reproduced using the information (Big Data) that affects…
Introduction The kernel trick is a typical method in machine learning to transform data from an original space to an arbitrary Hilbert space, usually of higher dimensions, where they are more easily separable (ideally, linearly separable). This technique is the…
Introduction Object detection is a branch of computer vision that focuses on identifying the presence and location of objects in images. It is used in a wide variety of applications, from surveillance systems, medical image analysis to autonomous driving. This…
Introduction In a previous blog article, we analyzed from a theoretical point of view which are the main libraries for data science and machine learning used by data scientists to treat data efficiently and extract its full potential. In this…
Introduction Data science is an interdisciplinary area that deals with extracting knowledge and useful information from data. Given the large amount of data generated by companies, it is important for data scientists to have tools that facilitate the manipulation, analysis…
Introduction Regression is a statistical methodology that describes the relationships between a continuous explained variable and a set of explanatory variables. In other words, regression models are able to predict the value of a dependent variable y with respect to…