nlp-architect - NLP Architect by Intel AI Lab: Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding

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NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. It is intended to be a platform for future research and collaboration. Framework documentation on NLP models, algorithms, and modules, and instructions on how to contribute can be found at our main documentation site.



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deep_qa - A deep NLP library, based on Keras / tf, focused on question answering (but useful for other NLP too)

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DeepQA is a library for doing high-level NLP tasks with deep learning, particularly focused on various kinds of question answering. DeepQA is built on top of Keras and TensorFlow, and can be thought of as an interface to these systems that makes NLP easier. DeepQA is built using Python 3. The easiest way to set up a compatible environment is to use Conda. This will set up a virtual environment with the exact version of Python used for development along with all the dependencies needed to run DeepQA.

DeepLearn - Implementation of research papers on Deep Learning+ NLP+ CV in Python using Keras, Tensorflow and Scikit Learn

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Implementation of research papers on Deep Learning+ NLP+ CV in Python using Keras, Tensorflow and Scikit Learn.

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.

spark-nlp - Natural Language Understanding Library for Apache Spark.

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John Snow Labs Spark-NLP is a natural language processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment. This library has been uploaded to the spark-packages repository .

deepnlp - Deep Learning NLP Pipeline implemented on Tensorflow

  •    Python

Deep Learning NLP Pipeline implemented on Tensorflow. Following the 'simplicity' rule, this project aims to use the deep learning library of Tensorflow to implement new NLP pipeline. You can extend the project to train models with your own corpus/languages. Pretrained models of Chinese corpus are distributed. Free RESTful NLP API are also provided. Visit for details. 下载预训练模型 If you install deepnlp via pip, the pre-trained models are not distributed due to size restriction. You can download full models for 'Segment', 'POS' en and zh, 'NER' zh, zh_entertainment, zh_o2o, 'Textsum' by calling the download function.

tensorlayer-tricks - How to use TensorLayer


While research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

one-pixel-attack-keras - Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

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How simple is it to cause a deep neural network to misclassify an image if an attacker is only allowed to modify the color of one pixel and only see the prediction probability? Turns out it is very simple. In many cases, an attacker can even cause the network to return any answer they want. The following project is a Keras reimplementation and tutorial of "One pixel attack for fooling deep neural networks".

snips-nlu - Snips Python library to extract meaning from text

  •    Python

Snips NLU (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts structured information. To find out how to use Snips NLU please refer to our documentation, it will provide you with a step-by-step guide on how to use and setup our library.

dynet_tutorial_examples - Tutorial on "Practical Neural Networks for NLP: From Theory to Code" at EMNLP 2016

  •    Python

A tutorial given by Chris Dyer, Yoav Goldberg, and Graham Neubig at EMNLP 2016 in Austin. The tutorial covers the basic of neural networks for NLP, and how to implement a variety of networks simply and efficiently in the DyNet toolkit.

t81_558_deep_learning - Washington University (in St

  •    Jupyter

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

AIDL-Series - :books: Series of Artificial Intelligence & Deep Learning, including Mathematics Fundamentals, Python Practices, NLP Application, etc


:books: Series of Artificial Intelligence & Deep Learning, including Mathematics Fundamentals, Python Practices, NLP Application, etc. 💫 人工智能与深度学习实战,机器学习篇 | Tensoflow 篇

tensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook

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This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

crfasrnn_keras - CRF-RNN Keras/Tensorflow version

  •    Python

This repository contains Keras/Tensorflow code for the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. This paper was initially described in an arXiv tech report. The online demo of this project won the Best Demo Prize at ICCV 2015. Original Caffe-based code of this project can be found here. Results produced with this Keras/Tensorflow code are almost identical to that with the Caffe-based version. The root directory of the clone will be referred to as crfasrnn_keras hereafter.

MemN2N-tensorflow - "End-To-End Memory Networks" in Tensorflow

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Tensorflow implementation of End-To-End Memory Networks for language modeling (see Section 5). The original torch code from Facebook can be found here. This code requires Tensorflow. There is a set of sample Penn Tree Bank (PTB) corpus in data directory, which is a popular benchmark for measuring quality of these models. But you can use your own text data set which should be formated like this.