Right Answer: The optimizer in TensorFlow is responsible for adjusting the model's parameters (weights and biases) during training to minimize the loss function, thereby improving the model's performance on the given task.
Right Answer: To handle overfitting in TensorFlow models, you can use techniques such as:
1. **Regularization**: Apply L1 or L2 regularization to the model's weights.
2. **Dropout**: Add dropout layers to randomly set a fraction of input units to 0 during training.
3. **Early Stopping**: Monitor validation loss and stop training when it starts to increase.
4. **Data Augmentation**: Increase the diversity of your training data by applying transformations.
5. **Reduce Model Complexity**: Use a simpler model with fewer layers or parameters.
6. **Cross-Validation**: Use k-fold cross-validation to ensure the model generalizes well.
