[baristaGeek]

Tensorflow implements the (really abstract, at least for me) concept of a computational graph. Think of it as nodes being values and edges operations.

Among other things, what this allows you to do is partition your computational graph into different subgraphs and run each subgraph on parallel.

Sklearn doesn't allow you to run things on parallel; however, I do agree that TF doesn't have a favorable learning curve, so you might want to start with SKlearn (or TFLearn) to get to know the basics of ML first.