As we witness the golden age of AI underpinned by deep learning, there are many different tools and frameworks continuously proposed. Sometimes it is even hard to catch up what is going on. You choose one over another then you see a new library and you go for it. However, it seems the exact choice is oblivious to anyone.
According to me, libraries are measured by flexibility and run-time trade-off. If you go with a library which is really easy to use, it is slow as much as that. If the library is fast, then it does not serve that much flexibility or it is so specialized to a particular type of models like Convolutional NNs.
After all the tears and blood dropped through years of experience in deep learning, I decide to share my own intuition and opinion about the common deep learning libraries so that these might help you to choose the right one for your own sake .
Let's start by defining some evaluations metrics for comparision. These are the pinpoints that I consider; Continue reading Comparison of Deep Learning Libraries After Years of Use