LLM: Large Language Models
Large Language Models (LLMs) are artificial intelligence systems designed to process and generate text from large volumes of language data. These models are built using deep neural networks, typically transformer architectures, which allow them to learn complex representations of words, sentences, and paragraphs. During the training process, the models […]
PCA: Principal Component Analysis
Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a data set while retaining the most relevant information. In more technical terms, PCA transforms a set of potentially correlated variables into a new set of variables, called principal components, that are linearly […]
KNN: K-Nearest Neighbors
K-Nearest Neighbors (KNN) is a supervised learning algorithm used for classification and regression. The basic principle of KNN is that similar objects tend to be close to each other in feature space. During the training phase, the algorithm stores all the data points from the training set. In the testing phase, […]
SVM: Support Vector Machines
Support Vector Machine (SVM) is a supervised learning algorithm used primarily for classification tasks, although it can also be applied to regression. The main goal of SVM is to find the optimal separating hyperplane that maximizes the margin between different classes in the feature space. The hyperplane is defined by a set of vectors […]
ANN: Artificial Neural Networks
Artificial Neural Networks (ANN) are a subfield of Artificial Intelligence (AI) that attempts to mimic the functioning of the human brain, not in terms of biology, but rather in terms of information processing. An ANN is composed of several processing units, known as neurons or nodes, organized in layers. The trio […]
RL: Reinforcement Learning
Reinforcement Learning (RL) is a branch of artificial intelligence and machine learning focused on training agents to make decisions in dynamic environments. The learning process is guided by rewards and punishments, where the agent interacts with the environment and receives feedback in the form of rewards. The goal is to maximize the […]
GAN: Generative Adversarial Networks
GANs (Generative Adversarial Networks) are a type of machine learning model that consists of two competing neural networks. The first network, called the generator, is responsible for creating synthetic data that attempts to replicate the distribution of a real dataset. The second network, […]
RNN: Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are a type of artificial neural network model designed to process sequences of data, where the order of the elements is crucial. Unlike feedforward neural networks, which process data in a single direction, RNNs have feedback connections that allow information from the past to influence the current output. This […]
CNN: Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are a type of deep learning neural network specifically designed to process data with an inherent grid structure, such as images. The architecture of CNNs consists of convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply filters (or kernels) to […]
NLP: Natural Language Processing
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling interaction between computers and humans using natural language. This field involves developing algorithms and models that enable computers to understand, interpret, and generate text and human speech accurately and contextually. Natural language processing […]
