In this tutorial, we will give you some deeper insights into recent developments in the field of Deep Learning NLP. The first part of the workshop will be an introduction into the dynamic deep learning library PyTorch. We will explain the key steps for building a basic model. In the second part, we will introduce how to implement more advanced architectures and apply it to real world datasets.
We will provide the notebooks listed in this repository (https://github.com/scoutbeedev/pytorch-nlp-notebooks/) for the attendees to follow through during the workshop. The demo will be done with Google Colab so there will no environment setup needed for the attendees. (make sure you have a google account) Throughout the tutorial, we won't explicitly go through all the notebooks but instead place focus on the slides which will show snippets from the notebooks. Major topics we want to cover are:
1) Intro to Pytorch
2) Build text classifier with Pytorch (Bag-of-Words and RNN based model)
3) Build a character text generator
4) Build a Seq2Seq model
5) Fine tuning with GPT-2