14 best open source encoder decoder projects.

We have collection of more than 1 Million open source products ranging from Enterprise product to small libraries in all platforms. We aggregate information from all open source repositories. Search and find the best for your needs. Check out projects section.

Displaying 1 to 14 from 14 results

Screenshot-to-code-in-Keras - A neural network that transforms a screenshot into a static website

Jupyter

This is the code for the article 'Turning design mockups into code with deep learning' on FloydHub's blog. Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.

keras deep-learning seq2seq encoder-decoder lstm floydhub machine-learning cnn cnn-keras jupyter-notebook jupyter

Simple Binary Encoding - Simple Binary Encoding (SBE) - High Performance Message Codec

Java

SBE is an OSI layer 6 presentation for encoding and decoding binary application messages for low-latency financial applications. This repository contains the reference implementations in Java, C++, Golang, and C#. The Java and C++ SBE implementations are designed with work very efficiently with the Aeron messaging system for low-latency and high-throughput communications. The Java SBE implementation has a dependency on Agrona for its buffer implementations.

codec c-plus-plus encoder-decoder serialization

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

Python

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton and Matt Post (2017): Sockeye: A Toolkit for Neural Machine Translation. In eprint arXiv:cs-CL/1712.05690.If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions to sockeye-dev-at-amazon-dot-com.

deep-learning deep-neural-networks mxnet machine-learning machine-translation neural-machine-translation encoder-decoder attention-mechanism sequence-to-sequence sequence-to-sequence-models sockeye attention-is-all-you-need attention-alignment-visualization attention-model seq2seq convolutional-neural-networks translation

neuralmonkey - An open-source tool for sequence learning in NLP built on TensorFlow.

Python

The Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.

neural-machine-translation tensorflow nlp sequence-to-sequence neural-networks nmt machine-translation mt deep-learning image-captioning encoder-decoder gpu

msgpack - easier, faster, but extendable MessagePack Serializer for Golang. / msgpack.org[Go]

This library is under the MIT License.

msgpack messagepack-serializer serializer encoder-decoder

Speech_Recognition_with_Tensorflow - Implementation of a seq2seq model for Speech Recognition using the latest version of TensorFlow

Jupyter

I uploaded three .py files and one .ipynb file. The .py files contain the network implementation and utilities. The Jupyter Notebook is a demo of how to apply the model. Seq2Seq model As I mentioned above the model architecture is similar to the one used in "Listen, Attend and Spell", i.e. we are using pyramidal bidirectional LSTMs in the encoder. This reduces the time resolution and enhances the performance on longer sequences.

speech-recognition speech-to-text tensorflow seq2seq encoder-decoder deep-learning machine-learning sequence-to-sequence nlp listen-attend-and-spell

Text_Summarization_with_Tensorflow - Implementation of a seq2seq model for summarization of textual data

Jupyter

Implementation of a seq2seq model for summarization of textual data using the latest version of tensorflow. Demonstrated on amazon reviews, github issues and news articles. I tried the network on three different datasets.

neural-network text-summarization text-summarizer seq2seq tensorflow nlp sequence-to-sequence encoder-decoder natural-language-processing deep-learning machine-learning summarization

DeepHarmonization - Demo code of the paper: "Deep Image Harmonization", Y

Python

Deep Image Harmonization Yi-Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu and Ming-Hsuan Yang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. This is the authors' demo code described in the above paper. Please cite our paper if you find it useful for your research.

computer-vision computational-photography image-editing deep-learning encoder-decoder

text-summarization-tensorflow - Tensorflow seq2seq Implementation of Text Summarization.

Python

Simple Tensorflow implementation of text summarization using seq2seq library. Encoder-Decoder model with attention mechanism.

tensorflow text-summarization seq2seq encoder-decoder

frontend-regression-validator - Visual regression tool used to compare baseline and updated instances of a website in a deployment pipeline

Python

The visual analysis computes the Normalized Mean Squared error and the Structural Similarity Index on the screenshots of the baseline and updated sites, while the visual AI looks at layout and content changes independently by applying image segmentation Machine Learning techniques to recognize high-level text and image visual structures. This reduces the impact of dynamic content yielding false positives. FRED is designed to be scalable. It has an internal queue and can process websites in parallel depending on the amount of RAM and CPUs (or GPUs) available.

website machine-learning deep-learning neural-network pipeline rest-api validator regression fred segmentation convolutional-neural-networks visual-regression encoder-decoder visual-regression-testing deployment-automation

basecrack - Decode All Bases - Base Scheme Decoder

Python

BaseCrack is a tool written in Python that can decode all alphanumeric base encoding schemes. This tool can accept single user input, multiple inputs from a file, input from argument, multi-encoded bases, bases in image EXIF data, bases on images with OCR and decode them incredibly fast. Decode Base16, Base32, Base36, Base58, Base62, Base64, Base64Url, Base85, Ascii85, Base91, Base92 and more with the best base encoding scheme decoding tool in town. It's useful for CTFs, Bug Bounty Hunting, and Cryptography (NOTE: Base Encoding is not an "Encryption" hence it doesn't fall under the Cryptography category, it's useful as base scheme encoding are often used in cryptography tools/projects/challenges).