Training the AI Model

Model training involves feeding the prepared data into the chosen algorithm to help it learn patterns and make accurate predictions. This process includes:

Initializing the Model: Setting up the algorithm with initial parameters. For complex models like neural networks, this may involve defining the architecture, such as the number of layers and neurons.

Feeding Data: The training data is input into the model in batches. Each batch helps the model adjust its internal parameters (weights) to minimize prediction errors.

Loss Function: A mathematical function that measures the difference between predicted and actual values. The goal of training is to minimize this loss.

Optimization Algorithms: Techniques like Gradient Descent are used to adjust model parameters iteratively to reduce the loss function. Variants like Stochastic Gradient Descent (SGD), Adam, and RMSprop are commonly used.

Epochs: Training typically occurs over multiple epochs, where the entire dataset is passed through the model several times to improve learning.

Model training requires careful monitoring to avoid overfitting (when the model learns noise instead of patterns) or underfitting (when the model is too simple to capture data complexity).