Steps
Description
Step 1: Dataset Selection
Choose a relevant, diverse dataset.
Step 2: Architecture Selection
Pick a suitable pre-trained model.
Step 3: Task-Specific Objective
Define a clear task and adapt the model.
Step 4: Hyperparameter Tuning
Adjust parameters for optimal performance.
Step 5: Training Process
Train the model and monitor performance.
Step 6: Regularization Techniques
Apply techniques like dropout and decay.
Step 7: Evaluation
Assess performance using relevant metrics.
