Hello from DeepMind! We've been watching from a distance and have been very happy to see the constant growth of leaderboard submissions. DeepMind is a great believer in leaderboards as a way to make progress on hard problems - whether that's measuring the score in an Atari game, the error in folding a protein or, as in this (possibly equally hard!) case, the accuracy of a machine learning model on a test set. We hope that the tutorial we provided was useful in getting an initial model running in Jax and would love any and all feedback:
* Was the tutorial easy to run on a colab instance?
* Did it cover enough to let you hit the ground running?
* Was the code easy to follow?
* Was anything particularly confusing on a first read?
DeepMind and Zindi are planning a webinar to be shared soon. We look forward to answering any questions you may have for us or going through the starter notebook.
AFAIR I had no particular issue running/understanding the tutorial which covered more than enough to get me started.
Thanks for co-hosting this challenge.
I ran the notebook as well, it was a fast smooth run and quite explanatory too. It was my first time using Jax and Haiku and I got the basic gist of it. so yeah, the code was easy to follow.
Thanks again.
The tutorial is cool but I am switching to Keras or Pytorch in order to use data augmentation. I did some research but I did not find something related to data augmentation with Jax.
Did you discover https://github.com/deepmind/dm_pix ?
We were considering including a section of the tutorial with some augmentation examples, but the tutorial was already feeling like it was quite long so we thought we'd err on the side of keeping it a bit more focused. We've made a note to include a list of likely useful Jax libraries in any future tutorials we put out, but lacking that https://project-awesome.org/n2cholas/awesome-jax seems to have a pretty good overview of what's available.
Not yet, but I will take a look. Thank you so much for sharing those interesting martials about Jax.