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    Question

    In deep learning, which type of neural network is most

    suitable for sequential data like time series?
    A Convolutional Neural Networks (CNNs) Correct Answer Incorrect Answer
    B Feedforward Neural Networks (FNNs) Correct Answer Incorrect Answer
    C Recurrent Neural Networks (RNNs) Correct Answer Incorrect Answer
    D Generative Adversarial Networks (GANs) Correct Answer Incorrect Answer
    E Radial Basis Function Networks (RBFNs) Correct Answer Incorrect Answer

    Solution

    Correct Option: Recurrent Neural Networks (RNNs) (C) are designed for handling sequential data by maintaining a memory of previous inputs, making them ideal for time series and sequence-based problems. Why Other Options Are Wrong: A) CNNs: Convolutional Neural Networks are mainly used for image processing tasks and are not suitable for sequential data. B) FNNs: Feedforward networks do not have memory, making them unsuitable for sequential data where prior inputs are important. D) GANs: GANs are used for generating new data based on learned distributions, not for processing sequences. E) RBFNs: These are used in certain kinds of regression and classification tasks, but they do not excel at handling sequential or time-series data.

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