Keras is so good that it is effectively cheating in machine learning, where even Tensorflow tutorials can be replaced with a single line of code. (which is important for iteration; Keras layers are effectively Lego blocks). A simple read of the Keras examples (https://github.com/fchollet/keras/tree/master/examples) and documentation (https://keras.io/getting-started/functional-api-guide/) will let you reverse-engineer most the revolutionary Deep Learning clickbait thought pieces.
It's good to see that backward compatability is a priority in 2.0, since it sounds like a lot had changed.
Trying to do something simple in TF is a pain, on the code there are some conflicting examples and code snippets that "train" a network just to print a loss number on the screen but actually do nothing besides that
Keras is easy to use and better if you're running CPU only
Keras now throws errors when trying to use some of the metrics they've deprecated. If you run into `UnboundLocalError: local variable 'class_name' referenced before assignment`, check that all your metrics are supported.
Keras also now ignores `nb_epoch` in favor of `epochs`. I must have misread the blog post, because I thought it would support `nb_epoch` for a bit and just use that in place of `epochs`.
That's the way it "felt" to me when it was introduced in one of the lessons (then later used in a project) in the first term of the Udacity Self-Driving Car Engineer nanodegree I am enrolled in (my cohort just finished the first term; second term for us starts on the 23rd).
We first played around with a simple NN library built from scratch in Python named "Miniflow"; basically, it was a relatively simple backprop library based around Numpy (to eliminate having to implement the vector math part of things). It gave a good overview of how a neural network is developed and implemented at a lower level, and how it actually worked. I say that with having a similar level of knowledge from taking the CS373 course at Udacity (2012) as well as the original (2011) "ML Class" from Andrew Ng - where in both a simple NN was developed using Python and Octave (respectively).
That gave us students the foundation; Tensorflow and how to use it with Python was then introduced (I also took the time and initiative to get my system prepped to use my 750 Ti GPU with TF). That was a revelation - TF made implementing a NN seemingly dead-simple by comparison. It felt almost like plug-n-play!
Then we learned about Keras: I thought that TF made things simple, but Keras proved that wrong. Your comment about it being "Lego blocks" is spot on. It was really simple to implement the NVidia End-to-End CNN to control a virtual car on a track, once it was given proper training from "camera" views.
All that said, though - without having that lower-level foundation of "Miniflow" - you can't appreciate exactly what it is that Keras gives you, nor can you easily grok what is actually happening under the hood, so to speak. I know that our simplified NN library only scratches the surface of what Tensorflow provides, but it does give a foundation on which to experiment and understand things further, IMHO.
Which is why just jumping into Keras without going back to "the roots" of neural networks and machine learning may be doing a disservice to self-learners on this topic. We are still at the point in the process where having the fundamental understandings can help to inform the implementer of solutions in Keras. Its kinda like knowing how to program in Java without understanding what a stack or a linked-list is, or how they work. While it is certainly possible to do so (and produce properly working code), for certain problems having that understanding may be a necessary requirement.
Even when it isn't, though, it still may be a worthwhile thing to know in the end. That's just my opinion, though.