Training

Definition

Training is the process of teaching a machine learning model to recognize patterns in data by adjusting its internal parameters (weights) based on examples. It involves feeding input-output pairs into the model so it can learn how to map inputs to correct outputs.

New model, historical data → learns patterns

Nuance

Training is computationally expensive and done in batches over many iterations (epochs). The model starts with random parameters and improves by minimizing a loss function using Gradient Descent or similar methods.

Examples

• Training GPT-4 on billions of text documents to learn language structure
• Teaching a vision model to identify cats by feeding it labeled images
• Fine-tuning a smaller model on customer support transcripts for a specific company

Real-world example

A self-driving car is trained by showing it thousands of videos labeled with road signs, pedestrians, and lane markings — so it can learn how to respond when it sees similar scenarios later.

Pros

• Enables the model to generalize beyond seen data
• Can be reused for inference across many domains
• Fine-tuning allows customization without starting from scratch
• Produces powerful models when given enough data

Cons

• Requires large datasets and compute resources
• Prone to overfitting if not regularized
• Training data biases can carry into the final model
• Takes significant time and experimentation to get right