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

•    Jupyter

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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

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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

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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

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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]

•    Go

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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

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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

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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

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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

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

tensorflow text-summarization seq2seq encoder-decoder

decoder-ring - CLI tool for decoding/encoding from common formats

•    Go

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Decoder-ring is a CLI tool for decoding/encoding from common formats. First install Go.

command-line-tool encoder-decoder

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

•    Python

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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

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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).

cryptography base64 tool decoder base32 base58 base16 decode infosec ctf capture-the-flag bugbounty base ctf-tools encoder-decoder decoders cryptography-tools decode-strings cryptography-project

json - Drop-in replacement for Golang encoding/json with additional features.

•    Go

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Drop-in replacement for Golang encoding/json with additional features. Same usage as Golang encoding/json.

json encoder-decoder