Deep Learning with Keras. Classification problem with large data I have looked on the internet and found a lot of discussion about image classification problems, but almost none of just classification problems without images. I am trying to build a model with Keras using Python, I have a dataset of 827 features (43 are integers, and the rest is one-hot encoded) and 24,800 samples. My target is 3 classes one-hot encoded as well ( [1 0 0] [0 1 0] [0 0 1] ).

I have found a lot of complex image recognition networks but only simple deep networks for non-image problems. I need help on how I can make the deep neural network architecture for my problem.
There are actually a lot of examples out there which show how to apply Keras to different types of data. A really good source is F. Chollet's book "Deep Learning with Python". The code is online. You could start with the tutorial on predicting house prices. It is a regression problem, but you can easily change it to a classification problem. 

A general remark: Neural nets (such as Keras) usually work well with very complex data (images, sound, text). For "normal" data, other approaches are often better suited (e.g. boosted trees). However, with the large number of features you have, NN is worth a try.