I've been learning a little about StyleGans lately and somebody told me that a Multi-Layer Perceptron, MLP, is used in parts of the architecture for transforming noise. When I saw this person's code, it just looked like a normal 8-layer fully connected network (i.e. linear-->relu-->linear-->relu-->...)
Last year, I read Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow 2 by Aurelien Geron and he talks about MLPs. When I read about it, I interpreted his description as that an MLP is not exactly the same as a vanilla fully connected neural network. I didn't fully understand the text and don't have the book anymore so, unfortunately, can't recall exactly what I read so I might have been completely wrong in my understanding of what he wrote.
Is an MLP the same thing as very basic fully connected network?