Training
The process of teaching a machine learning model to make predictions by exposing it to data and adjusting its parameters.
In-depth explanation
During training, the model sees examples from the training dataset and adjusts its internal parameters to minimize prediction errors. This involves forward propagation (making predictions), calculating loss (measuring errors), and backpropagation (adjusting parameters). Training continues for multiple epochs until the model converges or meets stopping criteria.
Examples
Related terms
More in AI Fundamentals
Artificial Intelligence (AI)
The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.
Machine Learning
A subset of AI where systems automatically learn and improve from experience without being explicitly programmed.
Deep Learning
A subset of machine learning using artificial neural networks with multiple layers to model complex patterns.
Algorithm
A set of step-by-step instructions or rules followed to solve a problem or perform a computation.
Model
A mathematical representation learned from data that can make predictions or decisions on new, unseen data.
Inference
The process of using a trained model to make predictions on new, unseen data.
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