Google Professional Machine Learning Engineer Question 104
Select 2Google Cloud PlatformYou are training a deep learning model using TensorFlow on Google Cloud AI Platform. The dataset is very large, and the training process is taking a significant amount of time. Which of the following techniques can you apply to make the training more efficient without compromising the model's performance?
- A
Use mixed precision training to utilize both float16 and float32 data types.
- B
Increase the batch size significantly to process more data per iteration.
- C
Enable Tensor Processing Unit (TPU) support to accelerate computation.
- D
Reduce the number of hidden layers in the neural network.
- E
Apply data augmentation techniques to artificially increase the size of the dataset.
Show answer and explanation
Correct answers: A, C
Explanation
Efficient training in machine learning requires leveraging techniques that reduce computation time while maintaining or improving model performance. Mixed precision training and TPUs directly enhance the efficiency of the training process, while other options either compromise performance or do not address training efficiency directly.
- A. Correct.
Mixed precision training leverages both float16 and float32 to speed up computation and reduce memory usage, making training more efficient without sacrificing accuracy. This is a recommended technique for large-scale models.
- B. Incorrect.
While increasing the batch size can improve training speed, doing so significantly can lead to instability in optimization and degrade generalization performance. This is not always an effective approach for efficient training.
- C. Correct.
Using TPUs can significantly accelerate the training process, especially for large-scale deep learning models, by providing specialized hardware for matrix operations.
- D. Incorrect.
Reducing the number of hidden layers simplifies the model but may compromise its ability to learn complex patterns, which is not ideal for maintaining performance.
- E. Incorrect.
Data augmentation is useful for improving model generalization but does not contribute to making the training process itself more efficient.