Deep-Learning-for-NLP

Contains different course tutorials and jupyter notebook file for applying different Deep Learning models in different NLP tasks such as text classification, summarization, translation, etc.

Contents

1. Introduction

basic concepts
Text representation, BoW, Word vectors

2. Text classification and Sentiment Analysis

Naive Bayes
Logistic Regression
fastText model
Deep models
- RNNs and LSTMs
- Convolutional neural networks for text classification
- RCNN (Recurrent convolutional neural networks for text classification
- AWD LSTMs and ULFiT approach
- Transformers (Bert, XLNet, etc.)

3. Neural Machine Translation

4. Text summarization

5. Other NLP tasks

Getting started

Reference