How do you decide between using CNNs, RNNs, or Transformers for your projects?

When deciding between CNNs, RNNs, or Transformers, I always start by looking closely at the nature of the data and the problem I’m trying to solve.

If I’m working with images or any data with a strong spatial structure, I usually turn to CNNs.

They do a great job of capturing local patterns like edges or textures, and I’ve found them incredibly effective for tasks like image classification and even some time series analysis when the structure is localized.

For tasks where sequence and order really matter, like text generation or speech modeling, RNNs used to be my go-to.

I’ve had success with LSTMs and GRUs, especially when training time is not a major concern and the sequences are of moderate length. However, RNNs tend to struggle with longer dependencies, and that is where Transformers have changed the game.

Nowadays, for most complex NLP tasks or anything requiring deep contextual understanding, I lean toward Transformers. Their self-attention mechanism allows them to handle long-range dependencies much more effectively than RNNs.

In my experience, they offer more flexibility and significantly better performance in large-scale language tasks.

So for me, it really comes down to understanding the structure of the input and the kind of relationships I need the model to learn. Over time, I have grown to appreciate the strengths of each architecture and have learned that the best results often come from choosing the right tool rather than just the most powerful one.