What are the best model architecture choices for AI?
The best model architecture choices depend on the specific task. For example, CNNs are great for image processing, while RNNs excel in sequence data.
Can you explain why CNNs are preferred for image tasks?
CNNs are designed to automatically and adaptively learn spatial hierarchies of features, making them effective for image recognition.
What about transformer models? When should I use them?
Transformers are ideal for tasks involving sequential data, like natural language processing, due to their attention mechanisms.
Are there any downsides to using transformers?
Yes, transformers can be computationally expensive and require large datasets to perform well, which might not be feasible for all applications.
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