By Considering these Chart of Complete Neural Networks Types:
And also here is a Commonly used Neural Network types in Keras I found, which I take note also for its most Practice Cases (You can correct me if I am wrong):
1 DNN (Deep Neural Network) → Most Practice Cases: Time series + predicting plain response y variable (flexible on numeric integer categoric
2 CNN (Convolution Neural Network) → Most Practice Cases: Time Series + Image Classification/Computer Vision
3 LSTM (Long Short-Term Memory) → Most Practice Cases: Time Series + NLP, → Recommended use for a long set of training data (More complex structure than GRU → LSTM has three gates (namely input, output and forget gates)
4 RNN (Recurrent Neural Network) → Most Practice Cases: Time Series + NLP, RNN → Recommended use for Sequence Data (faster training, computationally less expensive
5 GRU (Gated Recurrent Unit → Most Practice Cases: Time Series + NLP GRU → Recommended use for a shorter set of training data (Less complex structure than LSTM GRU has two gates (reset and update gates)
6 Auto Encoders (AE) → Most Practice Cases: Image Classification/Computer Vision (Noising & Denoising), Auto Encoders basically are a set of CNN, Pooling2D, and CNN-Transpose
Finally my Question:
Are there any types of Neural Network in above chart which structure of Network are currently not possible to build by
if there’s any Network which aren’t possible, could you point me what types, and why?
Are there any more Commonly used Neural Network aside from what I notes above? Appreciate it if theres any improvisation added to it
Appreciate any effort put into this question, Thank You!