What method can you employ to address this?

Topic 1, Main Questions Set A

Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly.

What method can you employ to address this?
A . Threading
B . Serialization
C . Dropout Methods
D . Dimensionality Reduction

Answer: C

Explanation:

Reference https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877