What are the different types of deep learning architectures?

What are the different types of deep learning architectures?

There are several types of deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). Each serves different purposes in tasks like image recognition, sequence prediction, and data generation.

Can you explain how CNNs work?

Sure! CNNs are designed to process data with a grid-like topology, such as images. They use convolutional layers to automatically detect features, pooling layers to reduce dimensionality, and fully connected layers for classification.

What about RNNs? How are they different?

RNNs are specialized for sequential data, like time series or natural language. They have loops in their architecture, allowing them to maintain a memory of previous inputs, which is crucial for tasks like language modeling.

What are GANs used for?

GANs consist of two networks, a generator and a discriminator, that work against each other. They're primarily used for generating realistic data, such as images, by learning from a training dataset.