Natural Language Processing: NLP With Transformers in Python

Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR.We cover several key NLP frameworks including:Hugging Face's Transformers TensorFlow 2Py Torchspa CyNLTK Flair And learn how to apply transformers to some of the most popular NLP use-cases:Language classification/sentiment analysis Named entity recognition (NER)Question and Answering Similarity/comparative learning Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:History of NLP and where transformers come from Common preprocessing techniques for NLP The theory behind transformers How to fine-tune transformers We cover all this and more, I look forward to seeing you in the course!