I think it's great that we're seeing a lot of experimentation in how to express models for machine learning. It's very clear we haven't found the best ways yet, and seeing what people choose to try to make easy to express (and harder) is good for progress.
(I'm still grouchy about the state of modularity within machine learning models. It's not easy enough to reuse / have libraries at the model level yet.)
I don't think the SKFlow author(s) are at Google. Prettytensor's are, but it isn't Google supported.
I haven't used tflearn or prettytensor, but I have used skflow (and a bit of raw TensorFlow).
SKFlow is nice if you are already using scikit learn because you can drop it straight into your sklean Pipelines[1]. This is great in terms of making it usable alongside other systems.
For example, I currently have a project using an ensemble of regression methods (2 different RandomForest regressors, and 3 XGB methods, then multiple different seeds for each method). SKFlow lets me drop in a TensorFlow regressor as well.
(In actual fact I can't get TF to perform as well as a RF on my featureset, and XGB outperforms it by far. This is using a relatively simple NN though).
I had doubts reading on Yann Lecun's fb feed that machine learning could be as big as the web, but all these jQuery-like libraries for ml have me second guessing.
I think the progress in neural networks is amazing. Every week the usage of tensorflow and friends seem to become a good chunk easier to use. Even people like me with close to no mathematical background can now spin up tensorflow and run a neural network. I'm loving it!
I would encourage you to try to use Tensorflow directly. Once you understand placeholders, variables, etc., it is not that much harder than most higher level wrappers.
It requires a bit more thinking ahead-of-time thinking, but also gives far more flexibility.
That said, I have found Keras to be excellent for quick experiments. Even more, because it supports both Tensorflow and Theano as backends.
http://tflearn.org/getting_started/#high-level-api-usage
Incredible, the Shakespearean text generator is only 42 lines long!
https://github.com/tflearn/tflearn/blob/master/examples/nlp/...