Natural Language Processing: NLP With Transformers in Python

Learn next-generation NLP with transformers for sentiment analysis, Q&A, similarity search, NER, and more

What you'll learn:

• Industry standard NLP using transformer models
• Build full-stack question-answering transformer models
• Perform sentiment analysis with transformers models in PyTorch and TensorFlow
• Advanced search technologies like Elasticsearch and Facebook AI Similarity Search (FAISS)
• Create fine-tuned transformers models for specialized use-cases
• Measure performance of language models using advanced metrics like ROUGE
• Vector building techniques like BM25 or dense passage retrievers (DPR)
• An overview of recent developments in NLP
• Understand attention and other key components of transformers
• Learn about key transformers models such as BERT
• Preprocess text data for NLP
• Named entity recognition (NER) using spaCy and transformers
• Fine-tune language classification models

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:

• HuggingFace's Transformers

• TensorFlow 2

• PyTorch

• spaCy

• NLTK

• 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!