HPO: Hyperparameter Optimization
Hyperparameter Optimization (HPO) is a fundamental process in the development of machine learning models that aims to find the best values for a model’s hyperparameters. Hyperparameters are parameters that are not learned during training, but are defined beforehand and directly influence the model’s performance. These include, for example, the rate of […]
EDA: Exploratory Data Analysis
Exploratory Data Analysis (EDA) is a statistical and data science approach that aims to explore and understand data sets through visual and quantitative techniques. The main goal of EDA is to discover patterns, anomalies, test hypotheses, and verify assumptions about the data. This process involves using […]
DV: Data Visualization
Data Visualization (DV) is an interdisciplinary field that combines elements of computer science, graphic design, cognitive psychology, and statistics to transform complex and voluminous data into understandable visual representations. These representations, which can be graphs, maps, dashboards, infographics, and other forms of visualization, facilitate the identification of patterns, trends, and […]
DP: Data Preprocessing
Data Preprocessing (DP) is a crucial step in the process of data analysis and machine learning model development. The goal of DP is to prepare raw data for more efficient analysis, ensuring that the data quality is adequate for model building and training. This process involves several steps, […]
DA: Data Augmentation
Data Augmentation (DA) is a technique widely used in the field of machine learning and data processing, particularly in deep learning tasks. The goal of DA is to increase the size of the training dataset by creating new data instances from existing examples. This is […]
FS: Feature Selection
Feature Selection (FS) is an essential technique in data processing and analysis, used to select a subset of relevant variables from a larger data set. The main goal is to identify and keep only those features that bring significant value to the modeling task, eliminating irrelevant or redundant features. This not only […]
FE: Feature Extraction
Feature Extraction (FE) is a fundamental process in artificial intelligence and machine learning that aims to identify and extract relevant information from raw data. This process transforms data into a format that can be easily analyzed by machine learning algorithms. FE involves detecting […]
DR: Dimensionality Reduction
Dimensionality Reduction (DR) is a technique used in machine learning and data analysis to simplify high-dimensional data sets, i.e. data that have a large number of features (variables). The main goal of DR is to transform the original feature space into a new one.
MVS: Multi-View Stereo
Multi-View Stereo (MVS) is a computer vision technique that allows the reconstruction of the three-dimensional environment from multiple two-dimensional images captured by cameras from different angles. The MVS process involves several steps, starting with the calibration of the cameras to determine their intrinsic and extrinsic parameters. Then, the detection and matching of […]
SFM: Structure from Motion
Structure from Motion (SFM) is a computer vision technique that allows 3D reconstruction of scenes from a series of 2D images. The process begins with capturing multiple images of a scene from different angles. SFM algorithms then identify corresponding points in the images and establish relationships between these points. […]
