Deep learning: Supervised learning [part 2]
In addition to classical classification and regression methods, there are other approaches and fields in which machine learning is used. Indeed, we can assign different labels to the same sample, create models to improve search in our applications, or suggest highly relevant content based on an individual user’s profile. Finally, we can build models that use data sequences to produce new sequences, such as translations or conversion from audio to text and vice versa.
Deep learning: Supervised learning [part 1]
When we need to analyze data, we have several techniques at our disposal. In deep learning, but more generally in data science, we can make use of some techniques called superivision learning. These consist of looking at some example data to predict values and/or labels on new data.
Deep learning: key concepts
Data scientists who want to use machine learning and/or deep learning techniques need to be clear about a few key concepts: data, models, objective functions, and optimization algorithms. Let’s analyze them in detail to understand how to use them in our projects.
Deep learning: introduction
Deep learning is a tool that has invaded everyday life. Many of the applications we use daily are based on models built with specific techniques that accumulate experience by looking at the data available to us. Let’s find out what it is and how it affects the use and experience of our applications.
NLP: a comprehensive guide [Part 3]
What are the NLP models that have made history? In this guide we will discover some of these as well as an overview of the most widely used libraries for developing models. Finally, we will cover some controversies that have arisen in this field.
NLP: a comprehensive guide [Part 2]
Natural language processing (NLP) allows us to create systems that can interpret what we write. But how are the data underlying these models processed? And what techniques are most commonly used? In this guide we will look at these issues.
NLP: a comprehensive guide [Part 1]
Every day we interface with computer systems that answer our questions in natural language. But how are the programmes of our day so ‘intelligent’ as to answer us? To answer this question, we need to understand what natural language processing is. This guide will introduce us to this branch of computer science.
PostGIS: introduction to the spatial database
Spatial data have become a key piece of information for many applications in recent years. Their use is no longer relegated to specific sectors. Therefore, the use of databases that allow efficient querying of these data types integrated with other information is essential. PostGIS represents the spatial relational database par excellence. Let’s discover its main features!
AI: engineering prompts to reduce hallucinations [part 2]
Hallucinations, i.e., responses that appear to make sense but are actually incorrect, afflict all large language models (LLMs). There are some techniques that can be used to mitigate this behavior. Let us discover some of them through examples and by analyzing the advantages and disadvantages.
AI: prompt engineering to reduce hallucinations [part 1]
Prompt engineering techniques allow us to improve the reasoning and responses provided by LLMs , such as ChatGPT. However, are we sure that the responses received are correct? In some cases no! When this happens, the model is said to have hallucinated. Let’s find out what this is and what are the techniques to reduce the probability of getting them.
