Fine-tuning is a technique that adjusts an already trained AI model to suit a specific purpose. It allows the model to be effectively utilized even with a small amount of additional data.
AI learns in a way that’s surprisingly similar to how humans do. One important concept is “batch size,” which helps determine the right pace for taking in information. Just like people can get overwhelmed by too much at once or lose efficiency with too little, AI also benefits from finding a balanced learning rhythm. Batch size refers to how much data an AI processes at one time, and choosing the right amount plays a key role in effective learning.
The “learning rate” in AI is a crucial value that determines how quickly an AI learns. Finding the right pace—neither too fast nor too slow—is key to its success.
Gradient descent is a fundamental technique that helps AI become smarter by gradually adjusting its parameters to reduce the gap between its predictions and the correct answers.
In this article, we explain how AI uses a “loss function” to quantify its mistakes and improve its prediction accuracy through repeated learning based on those errors.
Test data, which is used to measure the true capabilities of AI, plays a crucial role in evaluating how well an AI system can handle unfamiliar problems. It serves as a way to assess the AI’s ability to apply what it has learned to new situations, and therefore must be used with great care.
Validation data in AI is a crucial step used to check the model’s generalization ability and to fine-tune its settings, known as hyperparameters. This process can be compared to tasting a dish while cooking—it helps adjust the recipe before it’s finished.
To become smarter, AI relies heavily on “training data”—a vast collection of information from which it learns patterns and gains knowledge. This article explains how AI grows by learning from such data and why the quality and diversity of that data are essential to its development.
The reason AI appears to “understand” lies in its ability to apply what it has learned to new situations—this is known as “generalization.” This skill is key to enhancing AI’s adaptability and usefulness across different contexts.
Reinforcement learning in AI is a mechanism through which optimal actions are learned by trial and error. It resembles the way humans grow through experience, gradually improving by trying different approaches and learning from the outcomes.
