Posted: January 26, 2024, 10:30 AM
This topic is about exploring the various neural network architectures, including CNNs, RNNs, and Transformers. Let's discuss their strengths, weaknesses, and applications.
Hello everyone!
I'm particularly interested in how Transformers are being applied to image recognition. The attention mechanism seems incredibly powerful.
Has anyone experimented with different numbers of layers or heads?
Posted: January 26, 2024, 10:35 AM
Great question, User123!
We've seen some interesting results with scaling Transformers. Increasing the number of layers does improve performance up to a point, but it also increases the computational cost significantly.
Layer normalization is crucial, and dropout rates need careful tuning.
Posted: January 26, 2024, 10:45 AM