https://github.com/iperov/DeepFaceLab/blob/24eac44dd932664cf3a3fff6a017331a0791c6c2/mainscripts/Extractor.py#L245

for some edge cases, condition is not met and face is reported as not detected even if landmarks are aligned manually in gui and look ok.

No solution yet other than removing check.

We need a simple, up-to-date roadmap page on the website

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

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.

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

https://github.com/hunkim/PyTorchZeroToAll/blob/edd7ba1f19a8d2e74188d6caf6b020dc20130e53/03_auto_gradient.py#L7

The class torch.autograd.Variable is deprecated.

This line must be updated to

w = torch.tensor([1.0], requires_grad=True) # Any random value

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

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

In packaging process, README.md refer to me to execute this command

python package.py --dir=image_directories
                  --save_dir=binary_save_directory
                  --split_ratio=[0,1]

But, In implement, split_ratio option is float type, so Is it right to set split_ratio option to [0,1]?

I run this code

import os
os.environ['is_test_suite']="True" # this is writen due to bug for multiprocessing and pickling I issued. #426 
from auto_ml import Predictor
from auto_ml.utils import get_boston_dataset
from auto_ml.utils_models import load_ml_model

# Load data
df_train, df_test = get_boston_dataset()

# Tell auto_ml which column is 'output'
# Also note columns t