Deep learning for natural language processing: a gentle introduction

Introduction

The perceptron

Logistic regression

Implementing text classification using perceptron and logistic regression

Feed-forward neural networks

Best practices in deep learning

Implementing text classification with feed-forward networks

Distributional hypothesis and representation learning

Implementing text classification using word embeddings

Recurrent neural networks

Implementing part-of-speech tagging using recurrent neural networks

Contextualized embeddings and transformer networks

Using transformers with the hugging face library

Encoder-decoder methods

Implementing encoder-decoder methods

Neural architectures for natural language processing applications

Appendices: Overview of the Python language and key libraries ; Character encodings: ASCII and Unicode.