[FlyingSaucer]

I have used the MLP classifier[1] before. It's very simple to use (like most of sklearn's models). Worked well for standard and reasonably small classification model, but lacks some features for it to be a flexible way of using NNs:

- No saving checkpoints (can be crucial for large models who need alot of compute and time)

- No way to assign different activation functions to different layers

- No complex nodes like LSTM, GRU - No way to implement complex architectures like transformers, encoders etc

I also do not know if its even possible to use CUDA or any GPU with it.

[1] : https://scikit-learn.org/stable/modules/generated/sklearn.ne...

[kuu]

I would say the same as you. As long as you only need a simple model, yes, the MLP is good enough, but forget about making any DeepLearning stuff.

And AFAIK, there isn't GPU support, CPU performance is poor compared to GPU execution.