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.
Pandas: data analysis with Python [part 2].
Pandas is a Python library that allows us to analyze data from a variety of sources. Among the most useful features we surely find several functions to clean our data and extract some statistics about the distribution of values of various attributes. In addition, we can create aggregations with different logics and graph the data to extract more information. Let’s find out how to do all this with just a few lines of code!
Pandas: data analysis with Python [part 1].
Data scientists continually need to read, manipulate, and analyze data. In many cases they use specific tools, but sometimes they need to develop their own code. To do this, the Pandas library comes to our aid. Let’s learn about its data structures, how we can read data from different sources and manipulate it for our purposes.
Gradio: web applications in Python for AI [Part 3]
With Gradio, it is possible to create web applications for our machine learning and AI models in just a few lines of code. Through some examples, we will see the advanced features available, such as authentication, caching, and input file processing. We will also build a chatbot and an image classifier from pre-trained models. Finally we will discuss how to deploy our project in a few simple steps.
Gradio: web applications in python for AI [part2]
Gradio is a python library that allows us to create web applications quickly and intuitively for our machine learning and AI models. Our applications always require user interaction and layout customization. Let us find out, through examples, how to improve our applications.
Gradio: web applications in python for AI [part1]
Writing web applications for our machine learning and/or artificial intelligence models can take a lot of time and skills that we do not possess. To streamline and speed up this task we are helped by Gradio, a Python library designed to create web applications with just a few lines of code. Let’s discover its basic functionality with some examples.
