Implementation of research papers on Deep Learning+ NLP+ CV in Python using Keras, Tensorflow and Scikit Learn.
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Latest commit a69ba66 Apr 16, 2018


Welcome to DeepLearn. This repository contains implementation of following research papers on NLP, CV, ML, and deep learning. Visit my blog for more details - Deeplearn

- Latest Update : Added _deeplearn_utils modules. Check the releases for description of new features.

[1] Correlation Neural Networks. CV, transfer learning, representation learning. blog-post || code

[2] Reasoning With Neural Tensor Networks for Knowledge Base Completion. NLP, ML. blog-post || code

[3] Common Representation Learning Using Step-based Correlation Multi-Modal CNN. CV, transfer learning, representation learning. code

[4] ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs. NLP, deep learning, sentence matching. code

[5] Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks. NLP, deep learning, CQA. code

[6] Combining Neural, Statistical and External Features for Fake News Stance Identification. NLP, IR, deep learning. code

[7] WIKIQA: A Challenge Dataset for Open-Domain Question Answering. NLP, deep learning, CQA. code

[8] Siamese Recurrent Architectures for Learning Sentence Similarity. NLP, sentence similarity, deep learning. code

[9] Convolutional Neural Tensor Network Architecture for Community Question Answering. NLP, deep learning, CQA. code

[10] Map-Reduce for Machine Learning on Multicore. map-reduce, hadoop, ML. code

[11] Teaching Machines to Read and Comprehend. NLP, deep learning. code

[12] Improved Representation Learning for Question Answer Matching. NLP, deep learning, CQA. code

[13] External features for community question answering. NLP, deep learning, CQA. code

[14] Language Identification and Disambiguation in Indian Mixed-Script. NLP, IR, ML. blog-post || code

[15] Construction of a Semi-Automated model for FAQ Retrieval via Short Message Service. NLP, IR, ML. code


The required dependencies are mentioned in requirement.txt. I will also use dl-text modules for preparing the datasets. If you haven't use it, please do have a quick look at it.

$ pip install -r requirements.txt