Discover why deep learning is the ideal machine learning technique for image recognition and classification by extracting features from local image regions.
Table of Contents
Question
Which of the following machine learning techniques is ideal for image recognition and classification by extracting features from local regions of the image?
A. Logistic regression
B. Polynomial regression
C. Deep learning
D. Reinforcement learning
Answer
C. Deep learning
Explanation
Deep learning, specifically convolutional neural networks (CNNs), is ideal for image recognition and classification by extracting features from local regions of the image. Through convolutional 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.
Reinforcement learning is a technique that focuses on training agents through trial and error in an interactive environment. It is not specifically designed for image-recognition tasks.
Logistic regression is useful for classification tasks; however, it typically operates on numerical data and is not effective for processing and extracting features from complex image data.
Polynomial regression is similar to logistic regression and is not designed for image data. It struggles to capture the intricate spatial relationships within images.
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