Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
Awesome-NLP-Resources
This repository contains landmark research papers in Natural Language Processing that came out in this century.
https://github.com/Robofied/Awesome-NLP-Resources
- ](https://lbesson.mit-license.org/)
- How to read an academic article
- Advice on reading academic papers
- How to read and understand a scientific paper
- Should I Read Papers?
- The Refreshingly Rewarding Realm of Research Papers
- Sequence to Sequence Learning with Neural Network - LSTMN based approach for sequence problems.
- Learning Phase Representations using RNN Encoder-Decoder for statistical Machine Translation
- Attention Model(Neural Machine Translation By Jointly learning to Align and Translate) - Attention model architecture modified version for encoder decoder models (Don't confuse with [<i><b>Attention is all you need</b> paper</i>](#Transformers) i.e, for transformers concept)
- Understanding Back-Translation at Scale
- MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
- Scaling Neural Machine Translation
- The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
- Convolutional Sequence to Sequence Learning - Modified Attention model with convolutional layer
- Scalable Hierarchical Distributed Language Model
- Bag of Tricks for Efficient Text Classification - fastText(by Facebook AI Research) trained on billion of words for text classification.
- Language Models are Unsupervised Multitask Learners
- Hierarchical Probabilistic Neural Network Language Model - Speed up training and recogintion(by 200) - Yoshua Bengio
- Distributed Representations of Sentences and Documents - Sentence/Document to vectors by Tomas Mikolov by Google
- Distributed Representations of Words and Phrases and their Compositionality - WOrd2Vec representation by Tomas Mikolov(Google)
- Efficient Estimation of Word Representations in Vector Space - High quality vector representation from huge data sets by Tomas Mikolov(Google)
- Deep contextualized word representations - based on deep birectional Language Model by Allen Institute for Artificial Intelligence
- Enriching Word Vectors with Subword Information - Handles morphology and generates vectors for words not present in training dataset by Facebook AI Research <img src="fb-icon.png" width="20" height="20">
- Misspelling Oblivious Word Embeddings
- Neural Image Caption Generation with Visual Attention
- Deep Visual-Semantic Alignments for Generating Image Descriptions
- Attention Is All You Need
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - Multimodal Recurrent Neural Network architecture for image description by [Andrej Kaparthy <img src="andreaj.svg" width="20" height="20"> ](http://karpathy.github.io/) and Le-Fei-fei
- Google Machine Translation Blog
- Email AutoReply and Auto Suggestion
- Find Code errors and repair
- Image Captioning Using Keras
- The Illustrated Transformer - Transformers Research paper core details explained by [Jalammar](http://jalammar.github.io/)
- The Illustrated BERT - BERT is explained by [Jalammar](http://jalammar.github.io/)
- A Visual Guide to Using BERT for the First Time :boom: - Very beautifully explained BERT architecture with the help of visuals.
