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.
- 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...