Training Deep Learning Models

Training deep learning models requires significant computational power and large datasets. The process involves several steps:

1. Data Preparation: Cleaning, augmenting, and normalizing data to improve model learning.
2. Model Initialization: Defining the architecture and initializing weights randomly or using pre-trained models.
3. Forward Propagation: Passing input data through the network to obtain predictions.
4. Loss Computation: Comparing predictions with actual values using loss functions such as Cross-Entropy or Mean Squared Error.
5. Backward Propagation: Updating model parameters using techniques like Stochastic Gradient Descent (SGD) or Adam optimizer.
6. Validation and Testing: Evaluating the model on unseen data to assess generalization.

Proper training strategies, such as learning rate scheduling and dropout regularization, help prevent overfitting and improve model performance.