Question: A neural network is trained incrementally on small subsets of data which are called what? Answer this question from the below answer list: $1)$ Autocorrelation $2)$ Autoregressive Model $3)$ Bagging $4)$ Basis Models $5)$ Batches $6)$ Binary Cross Entropy $7)$ Boosting $8)$ Categorical Cross Entropy $9)$ Cross Validation $10)$ Data Preparation $11)$ Error Degrees Of Freedom $12)$ Explainability $13)$ Feature Engineering $14)$ Feature Importance $15)$ Fully Connected $16)$ Gradient Descent $17)$ Hard Voting $18)$ Hierarchical $($Multilevel$)$ Modeling $19)$ Hyperparameters $20)$ Learning Rate $21)$ Linear $22)$ Linear Programming $23)$ Logical Fallacy $24)$ Mean Squared Error $25)$ Model Evaluation $26)$ Periodicity $27)$ QDA $28)$ Recurrent Neural Network $29)$ Regularization $30)$ Reinforcement Learning $31)$ ReLU $32)$ Residuals Analysis $33)$ Sigmoid $34)$ Softmax $35)$ Structural Equation Modelling $36)$ Supervised Learning $37)$ SVM $38)$ Theta $39)$ Unsupervised Learning $40)$ Validation Set $41)$ Weights & Biases $42)$ XGBoostQuestion: A neural network is trained incrementally on small subsets of data which are called what? Answer this question from the below answer list: $1)$ Autocorrelation $2)$ Autoregressive Model $3)$ Bagging $4)$ Basis Models $5)$ Batches $6)$ Binary Cross Entropy $7)$ Boosting $8)$ Categorical Cross Entropy $9)$ Cross Validation $10)$ Data Preparation $11)$ Error Degrees Of Freedom $12)$ Explainability $13)$ Feature Engineering $14)$ Feature Importance $15)$ Fully Connected $16)$ Gradient Descent $17)$ Hard Voting $18)$ Hierarchical $($Multilevel$)$ Modeling $19)$ Hyperparameters $20)$ Learning Rate $21)$ Linear $22)$ Linear Programming $23)$ Logical Fallacy $24)$ Mean Squared Error $25)$ Model Evaluation $26)$ Periodicity $27)$ QDA $28)$ Recurrent Neural Network $29)$ Regularization $30)$ Reinforcement Learning $31)$ ReLU $32)$ Residuals Analysis $33)$ Sigmoid $34)$ Softmax $35)$ Structural Equation Modelling $36)$ Supervised Learning $37)$ SVM $38)$ Theta $39)$ Unsupervised Learning $40)$ Validation Set $41)$ Weights & Biases $42)$ XGBoost