Acronyms of data science

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Every field has its acronyms, machine learning does not avoid this rule. I am not a big fan of heavy acronyms usage, but if you happen to be lost in the middle of meeting, this may help!

Below is a list of acronyms, if I missed any, or if you have nice references regarding each topic, feel free to comment, I will do my best to keep this list as up-to-date as possible!

AE Auto encoder

AD Automatic differentiation

ANN Artificial Neural Network

API Application Programming Interface

ARD Automatic Relevance Determination

ASR Automatic Speech Recognition ASR

BPTT Back Propagation Through Time

BPTS Back Propagation Through Structure

BNN Binary Neural Net

COCO Common Objects in Context

CPPN Compositional Pattern-Producing Network

CTC Connectionist Temporal Classification

CNN Convolutional Neural network

CRF Conditional Random Field

CV Cross Validation

DBN Deep Belief Network

DCGAN deep convolutional generative adversarial networks

DNN Deep Neural Network

DT Decision tree

EBM Energy Based Model

ESP Enforced SubPopulations

ELU Exponential Linear Unit

ERF Extremly random forest

GAN Generative Adversarial Network

GBM Gradient Boosting Machine

GMM Gaussian Mixture Model

GRU Gated Recurrent Unit GRU

HMM Hidden Markov Model

NB Naive Bayes

NN Neural Network

KPCA Kernel Principal Component Analysis

KSVM Kernel Support Vector Machine

GA Genetic algorithm GA

HTM Heirarchal temporal memory

HMM Hidden Markov Model

HAM Hierarchical Attentive Memory

KNN k-Nearest Neighbors

LOOCV Leave one out cross validation

LReLU Leaky ReLU

LTU Linear Threshold Unit

LSTM Long Short Term memory

MCMC Markov chain Monte Carlo

MDP Markov Decision Processes

ML Machine Learning

MLP Multi-layer Perceptrons

NLP Natural Language Processing

NTM Neural Turing Machine

NEAT NeuroEvolution of Augmenting Topologies

OLS Ordinary Least Squares Regression

PReLU Paramaterized ReLU

OCR Optical Character Recognition.

PCA Principal Component Analysis.

PAC-MDP Probably Approximately Correct in Markov Decision Processes

RTRL Real Time Recurrent Learning

ReLU Rectified Linear Unit

RNN Recurrent Neural Network

RNTNRecursive Neural Tensor Network

RL Reinforcement Learning

RVM Relevance Vector Machine

ResNet Residual Neural Network

RF Random Forest

RBM Restricted Boltzmann Machines

SIFT Scale-Invariant Feature Transform

SRN Simple Recurrent Network

SVD singular value decomposition

SGD Stochastic Gradient Descent

SVM Support Vector Machine

SANE Symbiotic Adaptive NeuroEvolution

TDA Topological Data Analysis

TF TensorFlow

TFIDF Term Frequency Inverse Document Frequency

VLAD Vector of Locally Aggregated Descriptors

WFST Weighted Finite-State Transducers

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Published on February 25, 2022