Fast.ai library is amazing, but unfortunately, my company is still using TensorFlow for now, not even with Keras as the API.
I know that there are a lot of best practices built in our fast.ai library. I can see the difference when I am training on a dataset using Keras and when using fast.ai; fast.ai is just so much faster in terms of convergence speed. Naturally, I want to bring the best practices over to our company’s TensorFlow ecosystem, but there are so many of them and I am still not sure which of them TensorFlow + Keras is most lacking, and due to time constraint I could not simply just implement them all. So I have to choose the most important ones.
If you are going to rank by importance what fast.ai features & best practices I should bring over to a TensorFlow ecosystem to boost its training performance, what would the list look like?