NLP with Transformers: Introduction — Part 1

The first section of the thorough summary of the book Natural Language Processing with Transformers.

https://towardsdatascience.com/transformers-89034557de14

For Access: https://cbarkinozer.medium.com/nlp-with-transformers-introduction-part-1-e80684de43b2?source=friends_link&sk=2c9cb5f2845fb33fdddf2c17fb5d866c

If you can afford to buy and read the book [1,] I strongly advise you to do so rather than reading my notes. But if you can’t… let’s get started :)

This series will include 11 sections, to teach you:

• Learn how to build, debug, and optimize transformer models for core NLP tasks such as text categorization, named entity identification, and question answering.
• Learn how to use transformers for cross-lingual transfer learning.
  In real-world circumstances where labeled data is rare, use transformers.
• Make transformer models more deployable by employing approaches like distillation, pruning, and quantization.
• Learn how to train transformers from the ground up and scale them across many GPUs and distributed environments.

The Transformer architecture is so good at capturing patterns in long sequences of data and coping with large datasets that it is already being used for applications other than NLP, such as image processing.