The branch of computer science dealing with the reproduction, or mimicking of human-level intelligence, self-awareness, knowledge, conscience, and thought in computer programs.
I'm looking at the react tutorial at https://github.com/amark/gun/wiki/React-Tutorial and noticed that the code examples are using depreciated lifecycle methods such as componentWillMount.
When I run the example cartpole with the default parameters, it can not converge to the max reward 200, I wonder what went wrong.
Please indicate relevant versions, including, if relevant:
There seems to be conflicting data on what the "OriginGeopoint" is. In the documentation it's referenced as the location of the PlayerStart while in code it's commented as the coordinates of Unreal level at the coordinates 0,0,0.
Documentation:
https://micro
I followed the fine-tuning example described in here: https://github.com/pytorch/fairseq/blob/master/examples/mbart/README.md
However I didn't manage to reproduce the results described in the paper for EN-RO translation.
Add Azure notebook to our SETUP doc.
I tested google colab and Azure notebook to run reco-repo without requiring creating any DSVM or compute by myself, and it works really well with simple tweaks to the notebooks (e.g. for some libs, should install manually).
I think it would be good to add at least Azure notebook to our SETUP doc, where users can easily test out our repo w/o
Add trainer states so in each time user get info what is happening
simplify the return values and clean up the interface
The state will be implemented as enum and updated according to Trainer phase:
I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration✨ For existing plugins and projects, check out the spaCy universe.
If you have questions about the projects I suggested,