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awslabs / sockeye

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Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

machine-learning deep-neural-networks translation deep-learning mxnet machine-translation seq2seq convolutional-neural-networks neural-machine-translation sequence-to-sequence attention-mechanism encoder-decoder attention-model sequence-to-sequence-models attention-alignment-visualization attention-is-all-you-need sockeye
  • Updated Mar 16, 2020
  • Python

ufal / neuralmonkey

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Refactor concatenations of "variable" size lists before applying dense layers

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varisd
varisd commented Aug 7, 2018

Based on this line of code:
https://github.com/ufal/neuralmonkey/blob/master/neuralmonkey/decoders/output_projection.py#L125

Current implementation isn't flexible enough; if we train a "submodel" (e.g. decoder without attention - not containing any ctx_tensors) we cannot use the trained variables to initialize model with attention defined because the size of the dense layer matrix input become

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beginner-friendly refactor
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AnJoiner / FFmpegCommand

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FFmpegCommand适用于Android的FFmpeg命令库，实现了对音视频相关的处理，能够快速的处理音视频，大概功能包括：音视频剪切，音视频转码，音视频解码原始数据，音视频编码，视频转图片或gif，视频添加水印，多画面拼接，音频混音，视频亮度和对比度，音频淡入和淡出效果等

android ffmpeg fdk-aac encoder-decoder media-encoder lamemp3 jni-android libx264 ffmpegcommand
  • Updated Jan 23, 2020
  • Java

chimera0 / accel-brain-code

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The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) that I have written in my website. Especially, Natural Language Processing, Statistical Machine Learning, and Deep Reinforcement Learning are main topics.

reinforcement-learning deep-learning deep-reinforcement-learning q-learning lstm restricted-boltzmann-machine convolutional-autoencoder transfer-learning simulated-annealing deep-q-network automatic-summarization deconvolution combinatorial-optimization quantum-monte-carlo encoder-decoder auto-encoder quantum-annealing multi-agent-reinforcement-learning spatio-temporal-auto-encoder
  • Updated Mar 12, 2020
  • Python

zhongkaifu / Seq2SeqSharp

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Seq2SeqSharp is a tensor based fast & flexible encoder-decoder deep neural network framework written by .NET (C#). It has many highlighted features, such as automatic differentiation, many different types of encoders/decoders(Transformer, LSTM, BiLSTM and so on), multi-GPUs supported and so on.

translation deep-learning neural-network gpu machine-translation cuda transformer lstm seq2seq sequence-to-sequence tensor encoder-decoder attention-model transformer-encoder transformer-architecture
  • Updated Mar 10, 2020
  • C#

abdullahfarwees / screen-recorder-ffmpeg-cpp

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Create unique name for recorded videos

abdullahfarwees
abdullahfarwees commented May 12, 2018

Now application creates a video named "output.mp4". The task is to create a unique name in the following format "SR_DDMMYYYY_%%%.mp4"

DDMMYYYY represents the date, month and year.
%%% represents three digits start from 000 to a maximum of 999.

If the system date is 14-feb-2018, then the filename should be
example names are: SR_14022018_312.mp3

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enhancement good first issue help wanted

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