Concept graph
Glossary
Short definitions with deeper context and cross-links to sibling terms.
Machine Learning
adversarial-training
A method of training models to be robust against adversarial examples.
Data Augmentation
augment-data
A technique to artificially increase the size of a training dataset by creating modified copies of existing data.
Data Preparation
Augmented Data
Synthetic data created to enhance the diversity and quantity of training datasets.
Training Techniques
Backpropagation
An algorithm used for training neural networks by calculating gradients of the loss function.
Deep Learning
Batch Normalization
A technique to improve training speed and stability in deep neural networks by normalizing the inputs of each layer.
Machine Learning
batch-training
A method of training machine learning models using a subset of the dataset in each iteration.
Data Processing
Data Annotation
The process of labeling data for training machine learning models.
Data Processing
data-augmentation
The process of increasing the size and diversity of a training dataset by applying transformations.
Data Management
Dataset
A structured collection of data used for analysis and training machine learning models.
Data Management
Dataset Splitting
The process of dividing a dataset into training, validation, and test sets.
Machine Learning
distributed-learning
A machine learning paradigm where the training data is distributed across multiple devices or nodes.
Regularization
Dropout
A regularization technique used to prevent overfitting in neural networks by randomly deactivating a fraction of neurons during training.
Data Processing
feature-extraction
The process of transforming raw data into a set of usable characteristics for model training.
Data Processing
feature-selection
The process of selecting a subset of relevant features for model training.
Machine Learning
Generative Modeling
A type of modeling that generates new data instances that resemble the training data.
Deep Learning
generative-adversarial-networks
A class of machine learning frameworks that generate new data samples via adversarial training.
machine-learning
generative-models
Models that can generate new data instances similar to the training data.
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.
Machine Learning
incremental-learning
A machine learning approach that updates models continuously with new data without retraining from scratch.
Machine Learning
Model Training
The process of teaching a machine learning model to make predictions based on data.
modeling
model-trained
The process of training a machine learning model using data.
data-quality
noisy-labels
Labels in a dataset that are inaccurate or wrong, often leading to misguidance in model training.
machine learning
one-shot-learning
A machine learning approach that enables a model to learn information about object categories from a single training example.