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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!
Overview
