Natural Language Processing with Transformers in Python

Free Courses : Natural Language Processing 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 learn all you need to know 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!

1. Membuat halaman depan homepage facebook
2. Tutorial codeigniter untuk pemula
3. Membuat PDF dengan PHP
4. Auth pada Node dengan PassportJS
5. Statistics for Data Science and Business Analysis