**Training Processes** Training an AI model involves repeatedly adjusting its parameters to minimize a loss function. This process includes several key concepts: **Epochs:** * One complete pass through the entire training dataset. * During an epoch, the model updates its parameters multiple times based on the gradient descent algorithm. **Iterations:** * The number of times the model updates its parameters within an epoch. **Batches:** * Subsets of the training data that are processed sequentially. * Using batches helps improve efficiency by reducing the computational cost of each iteration. **Validation:** * Evaluating the model's performance on a separate dataset (validation set) that is not used for training. * Validation helps prevent overfitting and ensures the model generalizes well to unseen data. **Training Process:** 1. **Initialize Model Parameters:** Randomly or using pre-trained values. 2. **Forward Pass:** Calculate the model's output using the current parameters. 3. **Calculate Loss:** Measure the discrepancy between the output and the expected output. 4. **Backward Pass:** Calculate the gradients of the loss function with respect to the parameters. 5. **Parameter Update:** Adjust the parameters using an optimization algorithm (e.g., gradient descent). 6. **Repeat:** Continue steps 2-5 until the model reaches a convergence criterion (e.g., maximum number of epochs or a desired loss value). **Hyperparameter Tuning:** The effectiveness of a training process depends on the values of hyperparameters, which control the learning rate, batch size, and other aspects of the model. Hyperparameter tuning involves finding the optimal combination of values to achieve better performance.