Excuse me to write here, cant find another way to communicate with author.

Do you have a Russian manual sources? I'd like to make it (documentation) with Markdown, not PDF. I think it will be more github-ish way. You can contact me with solo12zw74 (at) yandex.ru.

I was thinking about implementing a custom controller for my setting but I'm not sure about which of the header files in firmware headers to include and/or inherit from as I can't find their related documentation nor comments inside the headers.

Looking at the con

Judging by the logic in https://github.com/horovod/horovod/blob/38e91bee84efbb5b563a4928027a75dc3974633b/setup.py#L1369 it is clear, that before installing Horovod one needs to install the underlying framework(s) (TensorFlow, PyTorch, ...).

This is not mentioned in the installation instructions which made me think, I can install Horovod and then any framework I like (or switch between them) and

Describe the bug
Most visualization api parameters documentation are not correctly formatted.

To Reproduce
Example of incorrect formatting of documentation: https://uber.github.io/ludwig/api/visualization/#learning_curves

**Ex

Instead of setting the dump_attention_no_plot parameter's value to "true"，you should use "dump_plots: false" to disable the attention plot.
config example:
python -m bin.infer \
--tasks "
- class: DecodeText
- class: DumpAttention
params:
output_dir: $PRED_DIR/attention
dump_plots: false" \
--model_dir $MODEL_DIR \
--batch_siz

In operations_broadcast_test.go there are some tests that are not yet filled in. The point is to test that broadcasting works for different shapes. The semantics of broadcast probably isn't clear, so please do send me a message for anything.

This is a good first issue for anyone looking to get interested

How to use Watcher / WatcherClient over tcp/ip network?

Watcher seems to ZMQ server, and WatcherClient is ZMQ Client, but there is no API/Interface to config server IP address.
Do I need to implement a class that inherits from WatcherClient?

Target objective:

Creating an ensemble using weights

Question

Looking at https://github.com/NervanaSystems/nlp-architect/blob/master/examples/supervised_sentiment/example_ensemble.py#L96-L97 how come the ensemble weights are training accuracies rather than validation accuracies? If the difference between acc and val_acc is great, wouldn't the ensemble overfit the training se

Concerning the tutorial here:
https://deeplearning4j.org/tutorials/04-feed-forward
A number of possible corrections I may or may not be right about:
The first example has an input and output layer but it says:
"As you can see above that we have made a feed-forward network configuration with one hidden layer. "

Also the input layer is a DenseLayer, not a FeedForwardLayer as I would assume it

pedantic, I know, but README.md has a typo in the Categorical Ensembling section where it refers to markets in the first sentence, not models.