MAB: Multi-Armed Bandit
The Multi-Armed Bandit (MAB) is a classic problem in decision theory and machine learning that models the situation in which an agent must choose, in each iteration, an action among several options, with the goal of maximizing the cumulative gain over time. Each action, or arm, provides a random reward, […]
RL: Reinforcement Learning
Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions through interaction with an environment. The agent’s goal is to maximize a cumulative reward over time. This process is guided by a reward function that provides feedback to the […]
FL: Federated Learning
Federated Learning (FL) is a distributed machine learning approach that allows multiple devices or clients to train a machine learning model collectively, without having to share their localized data. Instead of centralizing data on a server, FL sends the model to the devices, which then train the model locally using […]
TL: Transfer Learning
Transfer Learning (TL) is a technique in the field of Artificial Intelligence (AI) that allows a pre-trained model to be reused for a related task. Rather than starting from scratch, TL takes the knowledge a model acquired during training on an initial task and applies it to a new task, often saving time […]
NLG: Natural Language Generation
Natural Language Generation (NLG) is a subdomain of Artificial Intelligence that focuses on producing readable and coherent text from structured data, statistical models, and linguistic rules. NLG involves transforming data into textual narratives that are similar to human production. This process is divided into several steps: data analysis, […]
NLU: Natural Language Understanding
Natural Language Understanding (NLU) is a subfield of Artificial Intelligence (AI) that focuses on the ability of machines to interpret and understand human language, whether written or spoken. Unlike Natural Language Processing (NLP), which deals with the analysis and generation of natural language, NLU goes a step further, focusing on understanding […]
ASR: Automatic Speech Recognition
Automatic Speech Recognition (ASR) is a technology that allows computers to recognize and transcribe human speech into text. This process involves capturing audio, converting that audio into digital signals, and analyzing those signals to identify spoken words. ASR uses signal processing and machine learning algorithms to […]
TTS: Text-to-Speech
Text-to-Speech (TTS) is a technology that enables the transformation of written text into synthesized speech. This process involves several steps, starting with text analysis, going through pre-processing processes, language modeling and, finally, speech generation. Text analysis is crucial to understanding the structure and context of written language, […]
OCR: Optical Character Recognition
OCR (Optical Character Recognition) is a technology that allows the scanning and conversion of text from images into editable and searchable characters. The process involves several technical steps, starting with the capture of images of printed text, such as documents, photographs or handwritten fonts. These images are then processed by OCR algorithms that identify […]
CV: Computer Vision
Computer Vision (CV) is a branch of artificial intelligence that focuses on developing algorithms and techniques to enable machines and digital systems to interpret and understand the visual world as humans do. This field involves capturing, processing, analyzing, and interpreting images and videos, transforming pixels into meaningful information. Computer Vision […]
