Concept graph
Glossary
Short definitions with deeper context and cross-links to sibling terms.
Machine Learning
Active Learning
A machine learning paradigm where the model can query a user to obtain labels for new data points actively.
Security
adversarial-example
An input designed to fool a machine learning model into making incorrect predictions.
Machine Learning
Automated Machine Learning (AutoML)
A process that automates the end-to-end process of applying machine learning to real-world problems.
Machine Learning
AutoML
Automated Machine Learning streamlines the process of applying machine learning to real-world problems.
Machine Learning
Bagging
An ensemble method that improves the stability and accuracy of machine learning algorithms.
Machine Learning
batch-training
A method of training machine learning models using a subset of the dataset in each iteration.
Machine Learning
contextual-bandits
A form of machine learning that balances exploration and exploitation in dynamic environments.
Data Processing
Data Annotation
The process of labeling data for training machine learning models.
Data Preparation
Data Normalization
The process of scaling individual data points to a common scale, often to improve the performance of machine learning models.
Data Management
Dataset
A structured collection of data used for analysis and training machine learning models.
Machine Learning
Deep Learning
A subfield of machine learning that uses neural networks with many layers.
Machine Learning
distributed-learning
A machine learning paradigm where the training data is distributed across multiple devices or nodes.
Machine Learning
domain-adaptation
A technique in machine learning that aims to improve model performance on a target domain by leveraging labeled data from a related source domain.
Machine Learning
Feature Vector
A numerical representation of an object's characteristics used in machine learning.
Machine Learning
Federated Learning
A machine learning approach that allows models to be trained across decentralized devices or servers holding local data samples.
Machine Learning
few-shot-learning
A machine learning paradigm that trains models with very few labeled examples.
Deep Learning
Generative Adversarial Network
A class of machine learning frameworks where two neural networks contest with each other to create new data instances.
Deep Learning
generative-adversarial-networks
A class of machine learning frameworks that generate new data samples via adversarial training.
Machine Learning
Gradient Boosting
A machine learning technique that builds models in a sequential manner.
Optimization
Gradient Descent
An optimization algorithm used to minimize the loss function in machine learning.
machine learning
graph-learning
A branch of machine learning that focuses on learning from graph-structured data.
machine learning
graph-machine-learning
A branch of machine learning that focuses on learning from data represented as graphs.
Machine Learning
Hyperparameter Tuning
The process of optimizing the parameters that govern the training process of a machine learning model.
Machine Learning
Hyperparameters
Settings or configurations that govern the training process of a machine learning model.