Data & Training

Batch size is the number of training examples processed together in a single forward and backward pass during model training.

Data augmentation is a technique that artificially increases the size and diversity of a training dataset by creating modified versions of existing data samples.

A dataset is a structured collection of data points used to train, validate, or test machine learning models.

An epoch is one complete pass of a machine learning model through the entire training dataset during training.

In AI and machine learning, a feature is an individual measurable piece of data that serves as an input variable for a model.

Feature engineering is the process of transforming raw data into meaningful input variables (features) that help machine learning models learn patterns more effectively.

Fine-tuning is the process of taking a pre-trained AI model and continuing its training on a smaller, task-specific dataset to adapt it for a particular use case.

In machine learning, a label is the known correct output or category assigned to a training data example that a model learns to predict.

PEFT (Parameter-Efficient Fine-Tuning) is a family of techniques that adapt large pre-trained models to new tasks by updating or adding only a tiny fraction of parameters instead of retraining the entire model.

Pretraining is the first stage of training an AI model on a very large, general dataset so it learns broad patterns and representations before being adapted to specific tasks.

Synthetic data is artificially generated information designed to mimic the statistical properties of real-world data, created by algorithms rather than collected from actual events or observations.

Training is the process of feeding data into a machine learning model so it can learn patterns and adjust its internal parameters to make accurate predictions.

Training data is the dataset of examples that a machine learning model learns from during the training process. It contains input features paired with known outputs so the model can discover patterns.

A vector database is a specialized database designed to store and query high-dimensional vector embeddings, enabling fast similarity searches instead of traditional exact-match queries.