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ABC da Inteligência Artificial

334 termos de IA explicados em português simples, de A a Z.

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334 termos

A 7

  • Abstração de Dados
  • Acurácia: o que é e como calcular (com exemplo)
  • Algoritmo de Recomendação
  • Análise de Componentes Principais
  • Aprendizado Não Supervisionado
  • Aprendizado por Reforço
  • Aprendizado Supervisionado

B 12

  • Bandit Algorithms
  • Batch Learning
  • Batch Normalization
  • Batch Size
  • Bayesian Networks
  • Bias (Viés)
  • Biased Data
  • Bimodal Distribution
  • Biometria: o que é e o que a LGPD diz sobre ela
  • Blind Spot
  • Blob Detection
  • Bounding Box

C 9

  • Categorical Data
  • Causal Inference
  • Cloud AI
  • Computer Vision (Visão Computacional)
  • Confusion Matrix
  • Corpus
  • Cost Function
  • CUDA (Compute Unified Device Architecture)
  • Custom Model

D 14

  • Dados rotulados (labeled data): o que são, com exemplo
  • Data Augmentation
  • Data wrangling: o que é e quais são as etapas
  • Dataset
  • Decision Boundary
  • Deep Belief Network
  • Deep Q-Network
  • Deep Reinforcement Learning
  • Dendrogram
  • Dense Layer
  • Discriminant Analysis
  • Discriminative Model
  • Document Classification
  • Dynamic Programming

E 19

  • Early Stopping
  • Echo State Network
  • Eigendecomposition
  • Elastic Net
  • Elman Network
  • Emotion AI
  • Encoder
  • Encoder-Decoder Model
  • End-to-End Learning
  • Ensemble Learning
  • Ensemble Methods
  • Entity Disambiguation
  • Entity Linking
  • Error Rate
  • ETL (extract, transform, load): o que é e como funciona
  • Euclidean Distance
  • Expert System
  • Explanatory Variable
  • Exponential Smoothing

F 18

  • F1 Score
  • Fairness (equidade) em IA: o que é
  • False Discovery Rate
  • Falso positivo (false positive): o que é, com exemplo
  • Fast Fourier Transform
  • Fatoração de matriz não negativa (NMF): o que é
  • Feature Extraction
  • Feature Map
  • Feature Normalization
  • Feature Scaling
  • Feature Selection
  • Feature Vector
  • Federated Learning
  • Fitness Function
  • Forward Propagation
  • Frame Problem
  • Fully Connected Layer
  • Função softmax: o que é e para que serve

G 17

  • Game Theory
  • GAN (rede adversária generativa): o que é e como funciona
  • Gated Recurrent Unit (GRU)
  • Gaussian Distribution
  • Gaussian Mixture Model
  • Gaussian Noise
  • Gaussian Process
  • General AI
  • Generalização (generalization) em machine learning: o que é
  • Genetic Algorithm
  • Gini Impurity
  • Google AI
  • GPT (Generative Pre-trained Transformer)
  • Graphical Model
  • Greedy Algorithm
  • Grid Search
  • Ground Truth

H 15

  • Haar Cascades
  • Hamming Distance
  • Haptic Technology
  • Hashing
  • Hausdorff Distance
  • Hebbian Learning
  • Heteroscedasticity
  • Heurística: o que é em computação e IA
  • Hidden Layer
  • Hill Climbing
  • Hinge Loss
  • Histogram of Oriented Gradients (HOG)
  • Huber Loss
  • Hyperparameter Tuning
  • Hyperplane

I 10

  • i-vector
  • Image Classification
  • Inception Network
  • Incremental Learning
  • Inertial Measurement Unit
  • Inference
  • Interpolation
  • Isolation Forest
  • Item-Based Collaborative Filtering
  • Iterative Learning

J 12

  • Jaccard Index
  • Jacobian Matrix
  • JADE (Java Agent DEvelopment Framework)
  • JAGS (Just Another Gibbs Sampler)
  • Java AI APIs
  • Jensen Inequality
  • Jensen-Shannon Divergence
  • Jini Technology in AI
  • JIT Compilation
  • JPEG Compression
  • Julia Language for AI
  • Just-In-Time Learning

K 9

  • K-d Tree
  • Keras
  • Kernel PCA (Principal Component Analysis)
  • Kernel Regression
  • Kernel SVM (Support Vector Machine)
  • Key Performance Indicators (KPI) in AI
  • Keypoint Detection
  • Knowledge Base
  • Knowledge Management Systems

L 9

  • Layer Normalization
  • Leaky ReLU
  • Levenberg-Marquardt Algorithm
  • Lexicon (léxico): significado em IA e PLN
  • Linear Algebra in AI
  • Local Minima
  • Locality Sensitive Hashing
  • Log loss (logistic loss): o que é a perda logística em ML
  • Loss Function

M 13

  • MapReduce
  • Markov Decision Process
  • Mask R-CNN
  • Maximum Likelihood Estimation
  • Mean Squared Error
  • Meta Learning
  • Mini-Batch Gradient Descent
  • Missing Data Imputation
  • Model Evaluation
  • Model Optimization
  • Multi-Class Classification
  • Multi-Layer Perceptron
  • Multitask Learning

N 13

  • N-Gram
  • Named Entity Recognition
  • Natural Language Generation
  • Nested Cross-Validation
  • Nesterov Accelerated Gradient
  • Network Architecture
  • Network Pruning
  • Neural Architecture Search
  • Neural Rendering
  • Node
  • Noise Reduction
  • Noisy Data
  • Novelty Detection

O 16

  • Object Detection
  • Object Tracking
  • Off-Policy Learning
  • One-Hot Encoding
  • One-Shot Learning
  • Ontology
  • Ontology Learning
  • Open Set Recognition
  • OpenAI
  • Optimal Transport
  • Oracle
  • Orthogonalization
  • Out-of-Bag Error
  • Out-of-Core Learning
  • Outlier: o que é e o que fazer com ele
  • Overlapping Class Problem

P 17

  • Parameter Tuning
  • Partially Observable Markov Decision Process
  • Pattern Recognition
  • Pooling Layer
  • Pose Estimation
  • Precision
  • Precision-Recall Curve
  • Predictive Modeling
  • Preprocessing
  • Principal Component Analysis (PCA)
  • Probability Distribution
  • Production Deployment
  • Project Adam
  • Prolog in AI
  • Prototype Theory
  • Pseudo-Labeling
  • PSO (otimização por enxame de partículas): o que é

Q 9

  • Q-function
  • Q-Learning
  • Q-Value
  • Quadratic Discriminant Analysis
  • Quadratic Unconstrained Binary Optimization (QUBO)
  • Qualia in AI
  • Quasi-Newton Methods
  • Quaternion
  • Quotient Space Theory

R 13

  • R-squared
  • Radial Basis Function
  • Random Walk
  • RDF (Resource Description Framework)
  • Recurrent Neural Network (RNN)
  • Recursive Function
  • Region of Interest
  • ReLU (Rectified Linear Unit)
  • Residual Network (ResNet)
  • Reverse Engineering
  • Robustness
  • ROC Curve
  • Rule-based System

S 13

  • Sample
  • Self-Organizing Map
  • Sensitivity Analysis
  • Sequential Data
  • Siamese Network
  • Sparse Data
  • Speech Recognition
  • Spiking Neural Networks
  • Stackelberg Game
  • State of the art (estado da arte): o que significa em IA
  • Support Vector Machine (SVM)
  • Swarm Intelligence
  • Symbolic AI

T 19

  • T-distributed Stochastic Neighbor Embedding (t-SNE)
  • Tagging
  • Target Encoding
  • Temporal Convolutional Network
  • Tensor em IA: o que é, com exemplos
  • Tesseract OCR
  • Test Set
  • Text Mining
  • Thresholding
  • Topic Modeling
  • Topological Data Analysis
  • Tractability
  • Training Data
  • Training Epoch
  • Transfer Function
  • Triplet Loss
  • Truncated SVD
  • Trustworthy AI
  • Turing Test

U 14

  • U-Net
  • UIMA (Unstructured Information Management Architecture)
  • Unbiased Estimator
  • Under-sampling
  • Underfitting
  • Uniform Distribution
  • Unit Testing in AI
  • Universal Approximation Theorem
  • Unlabeled Data
  • Unseen Data
  • Up-Sampling
  • Uplift Modeling
  • User Experience (UX) Design in AI
  • User Interface (UI) Automation

V 11

  • Validation Set
  • Vanilla Neural Network
  • Vapnik-Chervonenkis (VC) Dimension
  • Variância (variance): o que é, como calcular e exemplo
  • Variational Inference
  • Virtual Reality
  • Vision Processing Unit (VPU)
  • Visual Question Answering
  • Visual SLAM (Simultaneous Localization and Mapping)
  • Viterbi Algorithm
  • Voice-to-Text

W 14

  • Walk-forward validation: validação em séries temporais
  • Wasserstein Distance
  • Wasserstein GAN
  • Watchdog Timer in AI Systems
  • Watson (IBM Watson)
  • Weak AI
  • Web Ontology Language (OWL)
  • Weight Matrix
  • Weighted Average
  • Whisker Plot
  • White Box Model
  • Wiener Filter
  • Working Memory
  • Wrapper Method

X 11

  • X86 Architecture in AI
  • XAI (eXplainable Artificial Intelligence)
  • Xavier Glorot Uniform
  • Xavier Initialization
  • Xception Architecture
  • Xenobot
  • XGBoost for Feature Importance
  • XGBoost Regression
  • XML Parsing
  • XOR Problem
  • Xtreme Gradient Boosting

Y 10

  • YAML for AI Configuration
  • YCbCr Encoding
  • YIQ Color Model
  • YOLO (You Only Look Once): o modelo de detecção de objetos
  • Yolov3
  • Yolov4
  • Yolov5
  • Yottabyte: quanto é (10^24 bytes) e de onde vem o nome
  • Yottaflops
  • YUV Color Space in Image Processing

Z 10

  • Z-normalization
  • Z-Score
  • Z-Slice in 3D Imaging
  • Zabbix: o que é e para que serve
  • Zepto-second Measurements in Ultrafast Computing
  • Zero Velocity Update in Sensor Fusion
  • Zero-R Learning Algorithm
  • Zero-Shot Learning
  • Zettabyte
  • Zope Object Publishing Environment

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