Yep, as he mentioned you need some familiarity with scikit learn or similar APIs, take a look at [1] for example. In essence X_train and Y_train are 2d arrays with shape (n_samples, n_features), Y_train is usually of shape (n_samples, 1) the same as the output. Normally both list of lists or numpy arrays are accepted, even a generator of samples as long as it is a 2D like structure. I would say that if this is not obvious to you maybe you should start with something more basic like linear models in scikit-learn before jumping to deep learning.
No need to be dismissive. The getting started guide linked to makes no mention of scikit - so yes, I don't even know what I don't know. scikit is not a prerequisite for machine learning, it's simply one way to approach it.
Sorry if it sounded like that. What I meant is that the 2d matrix representation with shape (n_samples, n_features) actually goes beyond scikit-learn and python(ex: dataframes in R or Julia), it is the standard representation of data in Machine Learning so it is assumed that someone who wants to do Deep Learning should already be familiar with it. That is why I thought you should start with something simpler than Deep Learning to get used to these concepts. Scikit-learn is a good option because it has more tutorials/examples/videos and more beginner friendly documentation in general.
[1] http://scikit-learn.org/stable/tutorial/basic/tutorial.html#...