Still, following it would require some familiarity with Numpy and scikit-learn (or other libs in the same spirit). As well as some experience with neural networks.
Yeah that's what I was referring to - still a lot of missing pieces. What type is X_train or Y_train? a list of dicts? Same question for prediction output?
I know enough about deep learning to grok the overall concepts and structure, but your docs aren't telling me anything about how to ACTUALLY get started with your lib.
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.
Still, following it would require some familiarity with Numpy and scikit-learn (or other libs in the same spirit). As well as some experience with neural networks.