I think CNTK and Tensorflow and Theano, too, have a declarative approach, representing the computation via a computational graph. Which in my opinion is beneficial for research. But for a hacker or a software developer who wants to build an application, this creates an unnecessarily steep learning curve and feels unintuitive. (I have the feeling, that this an important reason why Keras, Lasagne and co. exists)

Leaf takes an imperative approach and explores an easier API (only Layers (Functions)[1] and Solvers (Optimizer Algorithms)), reusability through modularity and abstractions that keep the implementation and concepts to a minimum or rather abstractions that feel as familiar to a hacker as possible.

For future versions e.g., we want to explore what is practically possible with auto-differentiation via dual numbers and differentiable programming.

[1]: http://autumnai.com/leaf/book/deep-learning-glossary.html#La...

My understanding is that Google developed Tensorflow to provide a single pipeline between the data science model and a production system. The idea is to avoid a translation step between modeling the real world and implementing it in production.

Of course, most people don't have the resources of Google with a layer of data scientists and another layer of software engineers [and maybe a layer of data engineers in the mix too]. So the idea of a tool tailored to a small team's needs rather than those of Google seems like an interesting niche.

There already is an explanation:

> Leaf is a Machine Intelligence Framework engineered by hackers, not scientists. It has a very simple API...

That is quite a diferentiator.