AI-900: How Does Deep Learning Identify Pixel Patterns in Image Classification Using CNNs?

Discover how deep learning, particularly Convolutional Neural Networks (CNNs), revolutionizes image classification by identifying intricate pixel patterns. Learn about their role in enhancing accuracy and advancing modern AI applications.

Question

In image classification, how does deep learning find pixel patterns conforming to specific classes?

A. Ordinary Least Squares (OLS)
B. Elastic Net
C. Using convolutional neural networks (CNNs)
D. Ridge Regression

Answer

C. Using convolutional neural networks (CNNs)

Explanation

Deep learning finds pixel patterns conforming to specific classes using convolutional neural networks (CNNs). Through these layers, CNNs automatically learn to identify and extract relevant features from the image data. These features capture low-level details such as edges and lines in the early layers, progressing to more complex shapes and object parts in deeper layers. CNNs comprise the following:

• Convolutional layers: These layers apply filters to the image, extracting features such as edges, shapes, and textures from local regions of the image. By stacking multiple convolutional layers, CNNs can learn increasingly complex hierarchical features.
• Pooling layers: These layers act like a summarization step, reducing the data’s dimensionality (size). This reduces computational complexity without losing important information.
• Fully connected layers: These layers integrate the extracted features from convolutional layers and perform the final classification task, assigning probabilities to different classes.

Ridge regression, ordinary least squares (OLS), and Elastic Net are linear regression techniques primarily used for numerical data, not image classification. They would not be suitable for capturing complex spatial relationships and patterns within image data.

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