What are the different architectures of AI models?

What are the different architectures of AI models?

AI models can be categorized into several architectures, including feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Each has unique strengths for different tasks.

Can you explain how a convolutional neural network works?

Certainly! A convolutional neural network (CNN) processes data with a grid-like topology, such as images. It uses convolutional layers to automatically detect features, pooling layers to reduce dimensionality, and fully connected layers for classification.

What are transformers and why are they popular?

Transformers are a type of model architecture that uses self-attention mechanisms to process data. They are popular for their efficiency in handling sequential data and have revolutionized natural language processing tasks.

How do I choose the right architecture for my project?

Choosing the right architecture depends on your specific task, data type, and performance requirements. For image tasks, CNNs are often best, while RNNs or transformers are preferred for sequential data like text.