I'm currently learning (pun intended) Deep Learning and I would like to apply it on a tabular dataset (.csv files). The dataset is labeled with "0" and "1" which means "normal" and "anomaly", respectively. The goal is to apply Deep Learning to perform the binary classification.
I came across and tried the following Deep Learning models: MLP, CNN, and LSTM. All of them work pretty well with Accuracy, Recall, Precision, Specificity, and F1-Score to be above 96%. However, I don't really understand the reason why and how the MLP, CNN, or LSTM can perform well on a tabular dataset? I Googled to find the reasoning, and I couldn't find any article about it. I did find the following articles Article 1 or Article 2 that mention that for tabular data the random forest or gradient boost techniques are better than Deep Learning. This article Article 3 mentions that MLP is good to be used in a tabular dataset, but there is no detailed explanation of why and how MLP can be good for a tabular dataset.
Therefore, my inquiry is please give me a more detailed explanation of why and how MLP, CNN, or LSTM can be good (or not) for a tabular dataset. Additional articles, papers, or books can also be recommended.
I thank you in advance.