What Does Model Training Mean?
What Does Model Training Mean?
What does model training mean? It can be defined as the process of teaching a machine learning or artificial intelligence algorithm to make predictions based on data. Consisting of data loading, preprocessing, algorithm selection, learning, and testing processes, model training is frequently used in areas such as data security and privacy, process optimization, customer service, employee adaptation, and decision-making support. You can also train an artificial intelligence model suitable for your organization to improve efficiency across many areas.
Model Training Processes
You can find all the details about model training processes step by step in the list below.
- Data Collection: To enable the artificial intelligence model to learn, large amounts of high-quality raw data in various formats, such as text, images, or audio, are collected.
- Preprocessing: The collected raw data is cleaned, organized, and converted into numerical formats that computers can understand.
- Selecting the Right Algorithm: At this stage, the type of data and the objective are the most important factors in determining the algorithm. An algorithm is selected according to data types such as text, images, or video and the intended purpose of the model, such as classification, prediction, or text generation.
- Training: The large language model, which begins learning in accordance with the defined criteria, repeatedly processes the same data, measures its error rate, and continuously updates its parameters during this process.
- Testing and Evaluation: Once the model has completed its basic learning process, it is presented with new patterns it has not encountered before and is encouraged to generate outputs based on these patterns and queries. In this way, the model is tested with entirely new data, allowing its accuracy and generalization ability to be measured.
