NLP has exploded in popularity over the last few years. But while
Google, Facebook, OpenAI, and others continue to release larger language
models, many teams still struggle with building NLP applications that
live up to the hype. This hands-on guide helps you get up to speed on
the latest and most promising trends in NLP.
With a basic understanding of machine learning and some Python
experience, you'll learn how to build, train, and deploy models for
real-world applications in your organization. Authors Ankur Patel and
Ajay Uppili Arasanipalai guide you through the process using code and
examples that highlight the best practices in modern NLP.
- Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP
tasks such as named entity recognition, text classification, semantic
search, and reading comprehension
- Train NLP models with performance comparable or superior to that of
out-of-the-box systems
- Learn about Transformer architecture and modern tricks like transfer
learning that have taken the NLP world by storm
- Become familiar with the tools of the trade, including spaCy, Hugging
Face, and fast.ai
- Build core parts of the NLP pipeline--including tokenizers,
embeddings, and language models--from scratch using Python and PyTorch
- Take your models out of Jupyter notebooks and learn how to deploy,
monitor, and maintain them in production
