AI-900 Question 104
Select 3A manufacturing company wants to use deep learning to detect defects in products on a conveyor belt. They have a large, labeled dataset of product images. Which of the following are key features of deep learning techniques that make them suitable for this task?
- A
Deep learning models can automatically extract features from raw data without manual intervention.
- B
Deep learning requires minimal computational resources compared to traditional machine learning techniques.
- C
Deep learning models perform well with large, labeled datasets to identify complex patterns.
- D
Deep learning is highly interpretable and provides clear reasoning for its decisions.
- E
Deep learning can leverage techniques such as convolutional layers to process image data efficiently.
Show answer and explanation
Correct answers: A, C, E
Explanation
Deep learning is particularly suited for defect detection in images because it can automatically extract features from raw data, handle large, labeled datasets effectively, and process image data efficiently using techniques such as CNNs. These capabilities make it a powerful tool for tasks involving complex visual patterns.
- A. Correct.
Deep learning techniques can automatically learn and extract features from raw data, such as edges, shapes, and patterns in images, making them highly effective for tasks like defect detection.
- B. Incorrect.
Deep learning requires significant computational resources, such as GPUs or TPUs, especially for training with large datasets. This statement is incorrect.
- C. Correct.
Deep learning excels at identifying complex patterns in large, labeled datasets, which is why it is well-suited for image classification tasks like defect detection.
- D. Incorrect.
Deep learning models are often considered black-box models, meaning they lack interpretability and do not provide clear reasoning for their decisions. This is incorrect.
- E. Correct.
Deep learning utilizes specialized architectures, such as convolutional neural networks (CNNs), which are highly effective for processing and analyzing image data.