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Question 172 - MLS-C01 discussion

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A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.

The company requires at least 85% accuracy to make use of the model.

After an exhaustive grid search, the optimal hyperparameters produced the following:

68% accuracy on the training set

67% accuracy on the validation set

What can the machine learning specialist do to improve the system's accuracy?

A.
Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
Answers
A.
Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
B.
Add more data to the training set and retrain the model using transfer learning to reduce the bias.
Answers
B.
Add more data to the training set and retrain the model using transfer learning to reduce the bias.
C.
Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
Answers
C.
Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
D.
Train a new model using the current neural network architecture.
Answers
D.
Train a new model using the current neural network architecture.
Suggested answer: B

Explanation:

The problem described in the question is a case of underfitting, where the neural network model performs poorly on both the training and validation sets. This means that the model has not learned the features of the data well enough and has high bias. To solve this issue, the machine learning specialist should consider the following change:

Add more data to the training set and retrain the model using transfer learning to reduce the bias: Adding more data to the training set can help the model learn more patterns and variations in the data and improve its performance. Transfer learning can also help the model leverage the knowledge from the pre-trained network and adapt it to the new data. This can reduce the bias and increase the accuracy of the model.

References:

Transfer learning for TensorFlow image classification models in Amazon SageMaker

Transfer learning for custom labels using a TensorFlow container and ''bring your own algorithm'' in Amazon SageMaker

Machine Learning Concepts - AWS Training and Certification

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asked 16/09/2024
om Kumar
42 questions
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