Do you know these AI Abbreviations?
Artificial Intelligence (AI) and Data Science are like the cool trendsetters, using data and smart tricks to change how industries work and help us make better decisions.
Now, as these AI and Data Science things keep growing, it’s all about teamwork and communication. And that’s where those short acronyms come in. They’re like little codes that experts use to talk quickly. Think of them as secret handshakes that make chatting easier. They’re not just for the pros, though.
This article is like a cheat sheet for all those acronyms in AI and Data Science. As AI and Data Science keep getting bigger and changing how the whole world works, knowing these shortcuts becomes more and more important. So let’s see them:
| Acronym | Description |
| ACC | ACCuracy |
| ACE | Alternating conditional expectation (ACE) algorithm |
| ADA | AdaBoosted Decision Trees |
| AdaBoost | Adaptive Boosting |
| AdR | AdaBoostRegressor |
| ADT | Automatic Drum Transcription |
| AE | AutoEncoder |
| AGI | Artificial General Intelligence |
| AI | Artificial Intelligence |
| AIWPSO | Adaptive Inertia Weight Particle Swarm Optimization |
| AM | Activation Maximization |
| AMT | Automatic Music Transcription |
| ANN | Artificial Neural Network |
| AR | Augmented Reality |
| ARNN | Anticipation Recurrent Neural Network |
| AUC | Area Under the (ROC) Curve |
| BDT | Boosted Decision Tree |
| BERT | Bidirectional Encoder Representation from Transformers |
| BiFPN | Bidirectional Feature Pyramid Network |
| BILSTM | Bidirectional Long Short-Term Memory |
| BLEU | Bilingual Evaluation Understudy |
| BN | Bayesian Network |
| BNN | Bayesian Neural Network |
| BP | BackPropagation |
| BPMF | Bayesian Probabilistic Matrix Factorization |
| BPTT | Backpropagation Through Time |
| BQML | Big Query Machine Learning |
| BRNN | Bidirectional Recurrent Neural Network |
| BRR | Bayesian Ridge Regression |
| CAE | Contractive AutoEncoder |
| CALA | Continuous Action-set Learning Automata |
| CART | Classification And Regression Tree |
| CAV | Concept Activation Vectors |
| CBI | Counterfactual Bias Insertion |
| CBOW | Continuous Bag of Words |
| CDBN | Convolutional Deep Belief Networks |
| CE | Cross-Entropy |
| CEC | Constant Error Carousel |
| CF | Common Features |
| CLNN | ConditionaL Neural Networks |
| CMAC | Cerebellar Model Articulation Controller |
| CMMs | Conditional Markov Model |
| CNN | Convolutional Neural Network |
| ConvNet | Convolutional Neural Network |
| CRBM | Conditional Restricted Boltzmann Machine |
| CRFs | Conditional Random Fields |
| CRNN | Convolutional Recurrent Neural Network |
| CTC | Connectionist Temporal Classification |
| CTR | Collaborative Topic Regression |
| CV | Coefficient of Variation |
| CV | Computer Vision |
| CSLR | Continuous Sign Language Recognition |
| DAAF | Data Augmentation and Auxiliary Feature |
| DAE | Denoising AutoEncoder or Deep AutoEncoder |
| DBM | Deep Boltzmann Machine |
| DBN | Deep Belief Network |
| DBSCAN | Density-Based Spatial Clustering of Applications with Noise |
| DCGAN | Deep Convolutional Generative Adversarial Network |
| DCMDN | Deep Convolutional Mixture Density Network |
| DE | Differential Evolution |
| DeconvNet | DeConvolutional Neural Network |
| DeepLIFT | Deep Learning Important FeaTures |
| DL | Deep Learning |
| DNN | Deep Neural Network |
| DQN | Deep Q-Network |
| DR | Detection Rate |
| DSN | Deep Stacking Network |
| DT | Decision Tree |
| DTD | Deep Taylor Decomposition |
| DWT | Discrete Wavelet Transform |
| ELECTRA | Efficiently Learning an Encoder that Classifies Token Replacements Accurately |
| ELM | Extreme Learning Machine |
| ELMo | Embeddings from Language Models |
| ELU | Exponential Linear Unit |
| EM | Expectation maximization |
| EMD | Entropy Minimization Discretization |
| ERNIE | Enhanced Representation through kNowledge IntEgration |
| ETL Pipeline | Extract Transform Load Pipeline |
| EXT | Extremely Randomized Trees |
| F1 Score | Harmonic Precision-Recall Mean |
| FALA | Finite Action-set Learning Automata |
| FC | Fully-Connected |
| FC-CNN | Fully Convolutional Convolutional Neural Network |
| FC-LSTM | Fully Connected Long Short-Term Memory |
| FCM | Fuzzy C-Means |
| FCN | Fully Convolutional Network |
| FFT | Fast Fourier transform |
| FLOP | Floating Point Operations |
| FLOPS | Floating Point Operations Per Second |
| FNN | Feedforward Neural Network |
| FNR | False Negative Rate |
| FPN | Feature Pyramid Network |
| FPR | False Positive Rate |
| FST | Finite state transducer |
| FWIoU | Frequency Weighted Intersection over Union |
| GA | Genetic Algorithm |
| GALE | Global Aggregations of Local Explanations |
| GAM | Generalized Additive Model |
| GAM | Global Attribution Mapping |
| GAN | Generative Adversarial Network |
| GAP | Global Average Pooling |
| GBRCN | Gradient-Boosting Random Convolutional Network |
| GD | Gradient Descent |
| GEBI | Global Explanation for Bias Identification |
| GFNN | Gradient Frequency Neural Networks |
| GLCM | Gray Level Co-occurrence Matrix |
| Gloss2Text | A task of transforming raw glosses into meaningful sentences. |
| GloVE | Global Vectors |
| GMM | Gaussian mixture model |
| GPR | Gaussian Process Regression |
| GPT | Generative Pre-trained Transformer |
| GradCAM | GRADient-weighted Class Activation Mapping |
| HamNoSys | Hamburg Sign Language Notation System |
| HAN | Hierarchical Attention Network |
| HCA | Hierarchical Clustering Analysis |
| HDP | Hierarchical Dirichlet process |
| HHDS | HipHop Dataset |
| hLDA | Hierarchical Latent Dirichlet allocation |
| HMM | Hidden Markov Model |
| HNN | Hopfield Neural Network |
| i.i.d | Independent and Identically Distributed |
| ID3 | Iterative Dichotomiser 3 |
| IDR | Input dependence rate |
| IIR | Input independence rate |
| INFD | Explanation Infidelity |
| IoU | Jaccard index (intersection over union) |
| ISIC | International Skin Imaging Collaboration |
| k-NN | k-Nearest Neighbor |
| KDE | Kernel Density Estimation |
| KL | Kullback Leibler (KL) divergence |
| kNN | k-Nearest Neighbours |
| KRR | Kernel Ridge Regression |
| LDA | Latent Dirichlet Allocation |
| LDA | Linear Discriminant Analysis |
| LDADE | Latent Dirichlet Allocation Differential Evolution |
| LIME | Local Interpretable Model-agnostic Explanations |
| LRP | Layer-wise Relevance Propagation |
| LSA | Latent semantic analysis |
| LSI | Latent Semantic Indexing |
| LSTM | Long Short-Term Memory |
| LTR | Learning To Rank |
| LVQ | Learning Vector Quantization |
| MADE | Masked Autoencoder for Distribution Estimation |
| MAE | Mean Absolute Error |
| MAF | Masked Autoregressive Flows |
| MAP | Maximum A Posteriori (MAP) Estimation |
| MAPE | Mean Absolute Prediction Error |
| MART | Multiple Additive Regression Tree |
| MaxEnt | Maximum Entropy |
| MCLNN | Masked ConditionaL Neural Networks |
| MCMC | Markov Chain Monte Carlo |
| MCS | Model contrast score |
| MDL | Minimum description length (MDL) principle |
| MDN | Mixture Density Network |
| MDP | Markov Decision Process |
| MDRNN | Multidimensional recurrent neural network |
| MER | Music Emotion Recognition |
| MINT | Mutual Information based Transductive Feature Selection |
| MIoU | Mean Intersection over Union |
| ML | Machine Learning |
| MLE | Maximum Likelihood Estimation |
| MLM | Music Language Models |
| MLP | Multi-Layer Perceptron |
| MPA | Mean Pixel Accuracy |
| MRR | Mean Reciprocal Rank |
| MRS | Music Recommender System |
| MSDAE | Modified Sparse Denoising Autoencoder |
| MSE | Mean Squared Error |
| MSR | Music Style Recognition |
| NAS | Neural Architecture Search |
| NB | Na ̈ıve Bayes |
| NBKE | Na ̈ıve Bayes with Kernel Estimation |
| NER | Named Entity Recognition |
| NERQ | Named Entity Recognition in Query |
| NF | Normalizing Flow |
| NFL | No Free Lunch (NFL) theorem |
| NLP | Natural Language Processing |
| NLT | Neural Machine Translation |
| NMS | Non Maximum Suppression |
| NN | Neural Network |
| NNMODFF | Neural Network based Multi-Onset Detection Function Fusion |
| NPE | Neural Physical Engine |
| NRMSE | Normalized RMSE |
| NST | Neural Style Transfer |
| NTM | Neural Turing Machine |
| ODF | Onset Detection Function |
| OLR | Ordinary Linear Regression |
| OLS | Ordinary Least Squares |
| PA | Pixel Accuracy |
| PACO | Poisson Additive Co-Clustering |
| PCA | Principal Component Analysis |
| PEGASUS | Pre-training with Extracted Gap-Sentences for Abstractive Summarization |
| PLSI | Probabilistic Latent Semantic Indexing |
| PM | Project Manager |
| PMF | Probabilistic Matrix Factorization |
| PMI | Pointwise Mutual Information |
| PNN | Probabilistic Neural Network |
| POC | Proof of Concept |
| POMDP | Partially Observable Markov Decision Process |
| POS | Part of Speech (POS) Tagging |
| PPMI | Positive Pointwise Mutual Information |
| PReLU | Parametric Rectified Linear Unit-Yor Topic Modeling |
| PYTM | Pitman |
| RandNN | Random Neural Network |
| RANSAC | RANdom SAmple Consensus |
| RBF | Radial Basis Function |
| RBFNN | Radial Basis Function Neural Network |
| RBM | Restricted Boltzmann Machine |
| ReLU | Rectified Linear Unit |
| REPTree | Reduced Error Pruning Tree |
| RF | Random Forest |
| RGB | Red Green Blue color model |
| RICNN | Rotation Invariant Convolutional Neural Network |
| RIM | Recurrent Interence Machines |
| RIPPER | Repeated Incremental Pruning to Produce Error Reduction |
| RL | Reinforcement Learning |
| RLFM | Regression based latent factors |
| RMSE | Root MSE |
| RNN | Recurrent Neural Network |
| RNNLM | Recurrent Neural Network Language Model (RNNLM) |
| RoBERTa | Robustly Optimized BERT Pretraining Approach |
| ROC | Received Operating Characteristic |
| ROI | Region Of Interest |
| RR | Ridge Regression |
| RTRL | Real-Time Recurrent Learning |
| SAE | Stacked AE |
| SARSA | State-Action-Reward-State-Action |
| SBM | Stochastic block model |
| SBO | Structured Bayesian optimization |
| SBSE | Search-based software engineering |
| SCH | Stochastic convex hull |
| SDAE | Stacked DAE |
| seq2seq | Sequence to Sequence Learning |
| SER | Sentence Error Rate |
| SGBoost | Stochastic Gradient Boosting |
| SGD | Stochastic Gradient Descent |
| SGVB | Stochastic Gradient Variational Bayes |
| SHAP | SHapley Additive exPlanation |
| SHLLE | Supervised Hessian Locally Linear Embedding |
| Sign2(Gloss+Text) | Sign to Gloss and Text |
| Sign2Gloss | A one to one translation from the single sign to the single gloss. |
| Sign2Text | A task of full translation from the sign language into the spoken one |
| SLP | Single-Layer Perceptron |
| SLRT | Sign Language Recognition Transformer |
| SLT | Sign Language Translation |
| SLTT | Sign Language Translation Transformer |
| SMBO | Sequential Model-Based Optimization |
| SOM | Self-Organizing Map |
| SpRay | Spectral Relevance Analysis |
| SSD | Single-Shot Detector |
| SSL | Self-Supervised Learning |
| SSVM | Smooth support vector machine |
| ST | Style Transfer |
| STDA | Style Transfer Data Augmentation |
| STL | Selt-Taught Learning |
| SVD | Singing Voice Detection |
| SVD | Singular Value Decomposition |
| SVM | Support Vector Machine |
| SVR | Support Vector Regression |
| SVS | Singing Voice Separation |
| t-SNE | t-distributed stochastic neighbor embedding |
| T5 | Text-To-Text Transfer Transformer |
| TD | Temporal Difference |
| TDA | Targeted Data Augmentation |
| TGAN | Temporal Generative Adversarial Network |
| THAID | THeta Automatic Interaction Detection |
| TINT | Tree-Interpreter |
| TLFN | Time-Lagged Feedforward Neural Network |
| TNR | True Negative Rate |
| TPR | True Positive Rate |
| TRPO | Trust Region Policy Optimization |
| ULMFiT | Universal Language Model Fine-Tuning |
| V-Net | Volumetric Convolutional neural network |
| VAD | Voice Activity Detection |
| VAE | Variational AutoEncoder |
| VGG | Visual Geometry Group |
| VPNN | Vector Product Neural Network |
| VQ-VAE | Vector Quantized Variational Autoencoders |
| VR | Virtual Reality |
| WER | Word Error Rate |
| WFST | Weighted finite-state transducer (WFST) |
| WMA | Weighted Majority Algorithm |
| WPE | Weighted Prediction Error |
| XAI | Explainable Artificial Intelligence |
| XGBoost | eXtreme Gradient Boosting |
| YOLO | You Only Look Once |
Credit: https://github.com/AgaMiko/machine-learning-acronyms
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