Update training tutorial
# What does this PR do?

This PRs rewrites the training tutorial that needed a bit of a refresher. It uses a simple example on the IMDB dataset (for basic text classification) with fine-tuning using:
- Trainer
- Keras
- Raw training loop in PyTorch

One last section with the raw training loop in TensorFlow could be added in a follow-up PR.
Re: freezing layers, someone pointed me to your answer here as well: From your deepspeed talk: https://youtu.be/RG-yV5zgqjQ?t=2450

I guess it's still a bit counterintuitive that the randomly initialized head doesn't cause havoc on the weights of base layers?  Just to make sure, it's not that you unfreeze the whole thing after only training the head first?  You just train the entire thing unfrozen from the very beginning?  

I've shared this with several folks and there was enough surprise that it could be worth mentioning this explicitly in this tutorial, as this is something I think lots of folks may not know!  

P.S. if it is the case that you unfreeze the entire thing from the very beginning this is such a surprising result I feel like it's worth a blog post or something!  <|||||>This is a surprise for someone who comes from the fastai community but insisting a lot on this for a user who don't even know what freezing layers is won't be helpful either, which is why I'm not mentioning it in the new version. I'll try to find a compromise between the two :-)

And yes, Transformers model are usually fine-tuned without freezing anything, which is what is done in all the research papers for GLUE/Squad etc. Training only the randomly initialized head usually does not achieve anything good and the state it ends in is so bad you can't recover by fine-tuning the whole model after.<|||||>@sgugger thank you for clarifying, and thanks for your patience!  I'm really glad I learned about this today as I have been doing it wrong all along.  

Seems like an interesting research project to find out "why" 