Displaying 1 to 9 from 9 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.

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.

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.

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.

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.

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.

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.

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.

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