Acronyms: Difference between revisions

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| '''[[AI]]''' ||  || [[Artificial intelligence]]
| '''[[AI]]''' ||  || [[Artificial intelligence]]
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| '''[[ASR]]''' ||  || [[Automatic speech recognition]]
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| '''[[AUC]]''' ||  || [[Area Under the Curve]]
| '''[[AUC]]''' ||  || [[Area Under the Curve]]
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| '''[[CNN]]''' ||  || [[Convolutional neural network]]
| '''[[CNN]]''' ||  || [[Convolutional neural network]]
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| '''[[CSV]]''' ||  || [[Comma-separated values]]
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| '''[[CUDA]]''' ||  || [[Compute Unified Device Architecture]]
| '''[[CUDA]]''' ||  || [[Compute Unified Device Architecture]]
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| '''[[MAPE]]''' ||  || [[Mean absolute percentage error]]
| '''[[MAPE]]''' ||  || [[Mean absolute percentage error]]
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| '''[[MNIST]]''' ||  || [[Modified National Institute of Standards and Technology]]
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| '''[[MSE]]''' ||  || [[Mean squared error]]
| '''[[MSE]]''' ||  || [[Mean squared error]]
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| '''[[ML]]''' ||  || [[Machine learning]]
| '''[[ML]]''' ||  || [[Machine learning]]
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| '''[[NER]]''' ||  || [[Named entity recognition]]
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| '''[[NLP]]''' ||  || [[Natural Language Processing]]
| '''[[NLP]]''' ||  || [[Natural Language Processing]]
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| '''[[NN]]''' ||  || [[Neural network]]
| '''[[NN]]''' ||  || [[Neural network]]
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| '''[[OCR]]''' ||  || [[Optical character recognition]]
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| '''[[OOF]]''' ||  || [[Out-of-fold]]
| '''[[OOF]]''' ||  || [[Out-of-fold]]
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| '''[[PCA]]''' ||  || [[Principal component analysis]]
| '''[[PCA]]''' ||  || [[Principal component analysis]]
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| '''[[PFE]]''' ||  || [[Probabilistic facial embedding]]
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| '''[[PPO]]''' ||  || [[Proximal Policy Optimization]]
| '''[[PPO]]''' ||  || [[Proximal Policy Optimization]]

Revision as of 21:14, 8 February 2023

See also: Guides, Terms and Abbreviations
ANN Artificial neural network
ANOVA Analysis of variance
AGI Artificial general intelligence
AI Artificial intelligence
ASR Automatic speech recognition
AUC Area Under the Curve
BERT Bidirectional Encoder Representations from Transformers
BLEU Bilingual evaluation understudy
CLIP Contrastive Language-Image Pre-Training
CNN Convolutional neural network
CSV Comma-separated values
CUDA Compute Unified Device Architecture
CV Computer Vision, Cross validation
DL Deep learning
DNN Deep neural network
DQN Deep Q-Learning
EDA Exploratory data analysis
GAN Generative Adversarial Network
GBM Gradient Boosting Machine
GLM Generalized Linear Model
GLUE General Language Understanding Evaluation
GPT Generative Pre-Training
GRU Gated recurrent unit
ICA Independent component analysis
KNN K-nearest neighbors
LAION Large-scale Artificial Intelligence Open Network
LaMDA Language Models for Dialog Applications
LLM Large language model
LLS Linear least squares
LSTM Long short-term memory
MAPE Mean absolute percentage error
MNIST Modified National Institute of Standards and Technology
MSE Mean squared error
ML Machine learning
NER Named entity recognition
NLP Natural Language Processing
NLU Natural Language Understanding
NMF Non-negative matrix factorization
NN Neural network
OCR Optical character recognition
OOF Out-of-fold
PaLM Pathways Language Model
PCA Principal component analysis
PFE Probabilistic facial embedding
PPO Proximal Policy Optimization
R2 R-squared
RF Random forest
REALM Retrieval-Augmented Language Model Pre-Training
RETRO Retrieval Enhanced Transformer
RFE Recursive Feature Elimination
RL Reinforcement learning
RLHF Reinforcement Learning from Human Feedback
RMSLE Root mean squared logarithmic error
RMSE Root mean squared error
RNN Recurrent neural network
ROC Receiver operating characteristic
RPA Robotic Process Automation
SL Supervised learning
STaR Self-Taught Reasoner
SVM Support vector machine
tf-idf term frequency–inverse document frequency
t-SNE t-distributed stochastic neighbor embedding
UL Unsupervised learning
ViT Vision Transformer