Enhance model training process explanation

Added detailed steps for model training process including data preparation, training, monitoring, and tuning.

Author: PrakshaaleJain

theory.md

@@ -936,7 +936,11 @@ Saving the weights learned by a model mid training for long running processes is
 
 **Can you tell us how you approach the model training process? ‍⭐️**
 
-Answer here
+Data Preparation :- Clean the data, scale features, and split it into Train, Validation, and Test sets. After this, choose the architecture & hyperparameters for the model.
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+Train & Monitor :- Run forward passes, compute loss, and update weights via backpropagation while tracking both training and validation loss.
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+Tuning & Regularization :- Adjust hyperparameters and apply Dropout, Weight Decay, or Early Stopping to prevent overfitting. Then, evaluate the final model on the unseen test set to check real-world performance.
 
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