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• 正解だと思う選択肢: DEF

  A: possible but unlikely for movie reviews B: wrong https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwi31N_10eX9AhWYQ0EAHXDFCAwQFnoECA8QAw&url=https%3A%2F%2Fdeepchecks.com%2Fquestion%2Fdoes-learning-rate-affect-overfitting%2F&usg=AOvVaw19RT-u_XyEe8FG_10R6aFC C: wrong because would increase complexity and potentially overfitting D: correct E: correct F: correct

  👍 5

  sevosevo2023/03/18
• 正解だと思う選択肢: DEF

  To improve the generalization of the deep learning sentiment analysis model and reduce overfitting, the following three solutions can be implemented:

  Add Dropout: Dropout is a regularization technique that randomly drops out (sets to zero) a certain percentage of nodes in the neural network during each training epoch. This helps to prevent overfitting and improve generalization.

  Add L1 and L2 Regularization: L1 and L2 regularization are techniques used to add a penalty to the loss function of the neural network, which helps to prevent overfitting. L1 regularization adds a penalty based on the absolute value of the weights, while L2 regularization adds a penalty based on the squared value of the weights.

  Decrease the number of layers in the network: Deep neural networks with too many layers can be prone to overfitting. Reducing the number of layers in the network can help to prevent overfitting and improve generalization.

  👍 3

  oso03482023/04/02
• 正解だと思う選択肢: DEF

  DEF are correct

  👍 2

  blanco7502023/03/20