Exam AWS Certified Machine Learning - Specialty topic 1 question 57 discussion

NO CORRECT TRAINING DATA, MORE WORKS JUST WASTE TIME. ONE OF THE REASONS FOR POOR ACCURACY COULD BE INSUFFICIENT DATA. THIS CAN BE OVERCOME BY IMAGE AUGMENTATION. IMAGE AUGMENTATION IS A TECHNIQUE OF INCREASING THE DATASET SIZE BY PROCESSING (MIRRORING, FLIPPING, ROTATING, INCREASING/DECREASING BRIGHTNESS, CONTRAST, COLOR) THE IMAGES. HTTPS://MEDIUM.COM/DATADRIVENINVESTOR/AUTO-MODEL-TUNING-FOR-KERAS-ON-AMAZON-SAGEMAKER-PLANT-SEEDLING-DATASET-7B591334501E ANSWER A. ADD MORE TRAINING DATA FOR ROTATION IMAGES COULD BE A WAY TO DEAL WITH ISSUE

A is answer

Data Augmentation would fix the missing conditional data

ChatGPT says the answer is A. Trust a model to answer an ML question correctly! ;)

how come more epochs it better than augmentation?

option A

The question is clear and the answer is clear as well

should be A

More epochs is not a good approach to fundamental data issues

the Specialist can apply data augmentation techniques to increase the training data by adding variation in rotation for training images. This technique will allow the model to learn to recognize cats in various orientations, including upside down.

Adding more variation in rotation to the training data can help the model to learn how to classify cats in different orientations, including when they are held upside down. This can improve the model's ability to identify cats in this position and reduce the misclassification rate for images in which the cats are upside down. By adding more rotation to the training data, the model can be trained to generalize better to new images, including those with cats in different orientations. This can help to reduce overfitting and improve the model's overall performance.

Only logical answer 100% A.

More data is a good answer. A

Answer is ”A””

Answer is A

This is a clear case of Data Augumentation solution.

Common step in CNN, Image augmentation. A.