I am attempting to run faceswap on an NVIDIA Jetson Nano board.

A few of the required faceswap dependencies didn't have pre-built binaries for this chip architecture so I manually compiled them, including:

• opencv-python==4.1.2.30
• numpy==1.17.4

The faceswap program runs ok and I can begin to train but it always eventuall

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.

This is an issue on the Quickstart web page. Sorry if this is not the right place for this type of issue report.

The page seems confusing regarding Maven configurations. It has a section called "Using DL4J In Your Own Projects: Configuring the POM.xml File", which sounds like the usual Maven dependencies information that so man

At all the pages have an "Edit on GitHub" link, but clicking that link leads to 404 from github.

For example, this page:
https://microsoft.github.io/AirSim/docs/px4_sitl/

Gives this broken link:
https://github.com/microsoft/airsim/edit/master/docs/docs/px4_sitl.md

Please use pytorch as shown in docs, otherwise users can get AssertionError: Torch not compiled with CUDA enabled.

E.g. in [app-seperation-semseg/Background-Grayscale.py](https://github.com/spmallick/learnopencv/blob/f99bdd938732a5e425dc5f799d56c6deb08913a3/app-seperation-semseg/Background-Graysc

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

We should generate a proper API documentation based on PyDoc strings. The question are:

1. How to make it look nice?
2. How to integrate it into the documentation?

Should finished #23 before doing this.

Mish is a novel activation function as developed by @digantamisra98 . The paper may be found here https://arxiv.org/abs/1908.08681

The task is to use property based testing to test the following features as listed in the paper:

• Non-monotonicity
• Non-monotonic derivatives
• Approximates half of identity at origin (do only float64 tests)

The original issue was raised in #362 and the

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:

Steps to objective:

nlp_architect/models/cross_doc_coref/system/cdc_utils.py

def load_mentions_vocab(mentions_files, filter_stop_words=False):
    logger.info('Loading mentions files...')
    mentions = []
    logger.info('Done loading mentions files, starting local dump creation...')
    for _file in mentions_files:
        mentions.extend(Menti

Issue Description

Please describe your issue, along with:

• expected behavior
• encountered behavior

Version Information

Please indicate relevant versions, including, if relevant:

• Deeplearning4j version
• platform information (OS, etc)
• CUDA version, if used
• NVIDIA driver version, if in use

Contributing

If you'd like to help us fix the issue by contributi

Probably too minor to mention, but just wanted to point out in case people notice it: since the notebooks indent with two spaces and Colab expects four spaces by default, Colab will make indented text red as a warning. e.g. from lab1/Part1_tensorflow.ipynb:

![image](https://user-images.githubuserconten