SeungAhSon/AI_Tutor_BERT · Datasets at Hugging Face

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Feature engineering
Attention mechanism
Singular value decomposition
Softmax function
Adversarial example
Adaboost
Activation function
Neural network
Multi-task learning
Loss function
Image segmentation
Inference
Unsupervised learning
Adversarial attack
Cross-validation
Convolutional neural network
Bias-variance tradeoff
Semi-supervised learning
Hyperparameter
Transfer learning
Ensemble learning
Deep learning
Instance-based learning
Alpha
Data augmentation
Weight initialization
Support vector machine
Evolutionary algorithm
Learning rate
Bag of words
Precision and recall
Zero-shot learning
Autoencoder
Backpropagation
Reinforcement learning
Active learning
Feedforward neural network
Gradient descent
Bayesian optimization
Label
Linear regression
Overfitting
Variational autoencoder
Embedding
Artificial intelligence
K-nearest neighbors
Capsule network
Federated learning
Principal component analysis
Time series analysis
Dropout
Clustering
Fine-tuning
Decision tree
Regression analysis
Validation
Sampling
Machine learning
Data preprocessing
Feature engineering or feature extraction or feature discovery is the process of extracting features (characteristics, properties, attributes) from raw data
Machine learning-based attention is a mechanism mimicking cognitive attention
In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix
The softmax function, also known as softargmax or normalized exponential function, converts a vector of K real numbers into a probability distribution of K possible outcomes
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks
AdaBoost, short for Adaptive Boosting, is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the 2003 G del Prize for their work
Activation function of a node in an artificial neural network is a function that calculates the output of the node (based on its inputs and the weights on individual inputs)
Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains.An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels)
A reference is a relationship between objects in which one object designates, or acts as a means by which to connect to or link to, another object
Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks
Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set
Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization
In statistics and machine learning, the bias variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model
Weak supervision, also called semi-supervised learning, is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone
Deep learning is the subset of machine learning methods which are based on artificial neural networks with representation learning
In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory
Alpha (uppercase , lowercase ; Ancient Greek: , lpha, or Greek: , romanized: lfa) is the first letter of the Greek alphabet
Data augmentation is a technique in machine learning used to reduce overfitting when training a machine learning model, by training models on several slightly-modified copies of existing data
In machine learning, the vanishing gradient problem is encountered when training artificial neural networks with gradient-based learning methods and backpropagation
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis
In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm
In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function
The bag-of-words model is a model of text represented as an unordered collection of words
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space
Zero-shot learning (ZSL) is a problem setup in deep learning where, at test time, a learner observes samples from classes which were not observed during training, and needs to predict the class that they belong to
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning)
As a machine-learning algorithm, backpropagation performs a backward pass to adjust a neural network model's parameters, aiming to minimize error
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward
Active learning is "a method of learning in which students are actively or experientially involved in the learning process and where there are different levels of active learning, depending on student involvement." Bonwell & Eison (1991) states that "students participate [in active learning] when they are doing something besides passively listening." According to Hanson and Moser (2003) using active teaching techniques in the classroom can create better academic outcomes for students
A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers
In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function
Bayesian optimization is a sequential design strategy for global optimization of black-box functions that does not assume any functional forms
A label (as distinct from signage) is a piece of paper, plastic film, cloth, metal, or other material affixed to a container or product, on which is written or printed information or symbols about the product or item
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables)

Feature engineering

Attention mechanism

Singular value decomposition

Softmax function

Adversarial example

Adaboost

Activation function

Neural network

Multi-task learning

Loss function

Image segmentation

Inference

Unsupervised learning

Adversarial attack

Cross-validation

Convolutional neural network

Bias-variance tradeoff

Semi-supervised learning

Hyperparameter

Transfer learning

Ensemble learning

Deep learning

Instance-based learning

Alpha

Data augmentation

Weight initialization

Support vector machine

Evolutionary algorithm

Learning rate

Bag of words

Precision and recall

Zero-shot learning

Autoencoder

Backpropagation

Reinforcement learning

Active learning

Feedforward neural network

Gradient descent

Bayesian optimization

Label

Linear regression

Overfitting

Variational autoencoder

Embedding

Artificial intelligence

K-nearest neighbors

Capsule network

Federated learning

Principal component analysis

Time series analysis

Dropout

Clustering

Fine-tuning

Decision tree

Regression analysis

Validation

Sampling

Machine learning

Data preprocessing

Feature engineering or feature extraction or feature discovery is the process of extracting features (characteristics, properties, attributes) from raw data

Machine learning-based attention is a mechanism mimicking cognitive attention

In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix

The softmax function, also known as softargmax or normalized exponential function, converts a vector of K real numbers into a probability distribution of K possible outcomes

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks

AdaBoost, short for Adaptive Boosting, is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the 2003 G del Prize for their work

Activation function of a node in an artificial neural network is a function that calculates the output of the node (based on its inputs and the weights on individual inputs)

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains.An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain

Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks

In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event

In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels)

A reference is a relationship between objects in which one object designates, or acts as a means by which to connect to or link to, another object

Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks

Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization

In statistics and machine learning, the bias variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model

Weak supervision, also called semi-supervised learning, is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them

In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone

Deep learning is the subset of machine learning methods which are based on artificial neural networks with representation learning

In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory

Alpha (uppercase , lowercase ; Ancient Greek: , lpha, or Greek: , romanized: lfa) is the first letter of the Greek alphabet

Data augmentation is a technique in machine learning used to reduce overfitting when training a machine learning model, by training models on several slightly-modified copies of existing data

In machine learning, the vanishing gradient problem is encountered when training artificial neural networks with gradient-based learning methods and backpropagation

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis

In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm

In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function

The bag-of-words model is a model of text represented as an unordered collection of words

In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space

Zero-shot learning (ZSL) is a problem setup in deep learning where, at test time, a learner observes samples from classes which were not observed during training, and needs to predict the class that they belong to

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning)

As a machine-learning algorithm, backpropagation performs a backward pass to adjust a neural network model's parameters, aiming to minimize error

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward

Active learning is "a method of learning in which students are actively or experientially involved in the learning process and where there are different levels of active learning, depending on student involvement." Bonwell & Eison (1991) states that "students participate [in active learning] when they are doing something besides passively listening." According to Hanson and Moser (2003) using active teaching techniques in the classroom can create better academic outcomes for students

A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers

In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function

Bayesian optimization is a sequential design strategy for global optimization of black-box functions that does not assume any functional forms

A label (as distinct from signage) is a piece of paper, plastic film, cloth, metal, or other material affixed to a container or product, on which is written or printed information or symbols about the product or item

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables)