What is the primary function of Convolutional Neural Networks (CNNs)?

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B) Image classification and recognition: Convolutional Neural Networks (CNNs) are primarily used in computer vision tasks such as image classification, object detection, and image recognition. They are designed to process data with grid-like topology, such as images, and excel at detecting spatial hierarchies in visual data using convolutional layers. CNNs are used extensively in tasks like facial recognition, identifying objects in images, and more.

a) Natural Language Processing (NLP): While NLP is a popular area of AI, it is not the primary function of CNNs. NLP tasks typically use Recurrent Neural Networks (RNNs) or transformers like BERT, GPT, etc., rather than CNNs. NLP focuses on understanding and processing human language, which is quite different from the image-focused tasks CNNs excel at.
c) Time series forecasting: Time series forecasting typically involves predicting future values based on historical data. This is more commonly done using models like Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), or even ARIMA models, not CNNs. CNNs are not optimized for sequential data where time or order is crucial, unlike RNNs.
d) Reinforcement learning: Reinforcement learning involves training an agent to make decisions in an environment by receiving feedback through rewards or punishments. It is not directly related to CNNs. Reinforcement learning is more about decision-making and learning from interactions, while CNNs are used for tasks related to pattern recognition in images and other grid-like data.