regularizer function
正則化函數
using a regularizer
使用正則化
regularizer term
正則化項
add regularizer
添加正則化
regularizer weight
正則化權重
regularizer penalty
正則化懲罰
regularizer effect
正則化效果
regularizer parameter
正則化參數
the regularizer helped prevent overfitting in the model.
正則化器有助於防止模型過擬合。
we used an l1 regularizer to encourage sparsity.
我們使用L1正則化器來鼓勵稀疏性。
adding a regularizer can improve generalization performance.
添加正則化器可以提高泛化性能。
the weight decay regularizer penalized large weights.
權重衰減正則化器懲罰了大的權重。
we compared models with and without a regularizer.
我們比較了帶有和不帶正則化器的模型。
the regularizer minimized the complexity of the neural network.
正則化器最小化了神經網絡的複雜性。
ridge regression employs an l2 regularizer.
嶺迴歸使用L2正則化器。
elastic net combines l1 and l2 regularizers.
彈性網絡結合了L1和L2正則化器。
the optimal regularizer parameter was determined empirically.
最優的正則化器參數是經驗確定的。
a strong regularizer can lead to underfitting.
強大的正則化器可能導致欠擬合。
regularizer function
正則化函數
using a regularizer
使用正則化
regularizer term
正則化項
add regularizer
添加正則化
regularizer weight
正則化權重
regularizer penalty
正則化懲罰
regularizer effect
正則化效果
regularizer parameter
正則化參數
the regularizer helped prevent overfitting in the model.
正則化器有助於防止模型過擬合。
we used an l1 regularizer to encourage sparsity.
我們使用L1正則化器來鼓勵稀疏性。
adding a regularizer can improve generalization performance.
添加正則化器可以提高泛化性能。
the weight decay regularizer penalized large weights.
權重衰減正則化器懲罰了大的權重。
we compared models with and without a regularizer.
我們比較了帶有和不帶正則化器的模型。
the regularizer minimized the complexity of the neural network.
正則化器最小化了神經網絡的複雜性。
ridge regression employs an l2 regularizer.
嶺迴歸使用L2正則化器。
elastic net combines l1 and l2 regularizers.
彈性網絡結合了L1和L2正則化器。
the optimal regularizer parameter was determined empirically.
最優的正則化器參數是經驗確定的。
a strong regularizer can lead to underfitting.
強大的正則化器可能導致欠擬合。
Explore frequently searched vocabulary
Want to learn vocabulary more efficiently? Download the DictoGo app and enjoy more vocabulary memorization and review features!
Download DictoGo Now