With the data prepared and the model architecture designed, the next critical step is model training and optimization. This phase involves feeding the prepared data to the chosen AI model, allowing it to learn patterns, relationships, and representations from the data. The goal is to optimize the model’s internal parameters (weights and biases) so that it can accurately perform the desired task, whether it’s classification, regression, or another AI function. This is an iterative process that often requires careful monitoring and adjustment.
Training Methodologies and Techniques #
Model training involves exposing the model to the training dataset and iteratively adjusting its parameters to minimize a predefined loss function. The choice of training methodology and techniques depends on the type of model, the size of the dataset, and computational resources.
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