text: **USER**: Namaste, AI sahab. Main aap se ek technical sawal poochna chahta hoon.
  
  **ASSISTANT**: Dhanyavaad, aap mujhse pooch sakte hain. Main aapki madad karne ke liye yahan hoon.
  
  **USER**: Main ek machine learning model train kar raha hoon, aur mujhe overfitting se pareshaan ho raha hoon. Aap mujhe bata sakte hain ki overfitting se kaise bacha ja sakta hai?
  
  **ASSISTANT**: Overfitting se bachne ke liye, aap ye tarike try kar sakte hain:
  
  * **Regularization**: Regularization techniques add a penalty term to the loss function that encourages the model to find simpler solutions. This helps prevent overfitting by discouraging the model from learning overly complex patterns in the data.
  
  * **Early stopping**: Early stopping involves monitoring the model's performance on a validation set during training. When the model's performance on the validation set starts to degrade, training is stopped to prevent overfitting.
  
  * **Data augmentation**: Data augmentation involves creating new training data by applying random transformations to the existing data. This helps the model learn more generalizable features and reduces the risk of overfitting.
  
  * **Dropout**: Dropout is a technique that randomly drops out neurons during training. This helps prevent the model from learning overly dependent relationships between features.
  
  * **Cross-validation**: Cross-validation involves splitting the training data into multiple folds and training the model on different combinations of these folds. This helps estimate the model's generalization error and reduces the risk of overfitting.
  
  **USER**: Ye tips bahut helpful hain. Dhanyavaad, AI sahab.
  
  **ASSISTANT**: Aapka swagat hai. Agar aapko aur koi sawal ho, toh mujhse poochne mein sankoch mat karna.
seed: ts of New York, Manhattan where nearly silent. The Tower of Stark Industries, or Avengers Tower was dark and silent and still and that was good. It was dead like he was. He liked the darkness. It didn't hurt his eyes so much. Who was he? He didn't rightly know. He had been called many things, in the past and more recently. So far he was content with naming himself as he had been named in the past The Asset. Though he had been called other things, the Weapon, the Boy, the Drone, the Winter Soldier. Recently he had also been called Bucky by one, a Target. His Mission had called him Bucky and this had made him hesitate, falter though h
usecase: Act as an expert in a specialized field and provide detailed, technical explanations and references in response to complex inquiries.
  Generate engaging titles for written content based on provided topics and keywords.
  Act as a simulated environment and respond with appropriate outputs based on user-provided commands.