Data Science: Transformers for Natural Language Processing

Hello friends!

Welcome to Data Science: Transformers for Natural Language Processing.

Ever since Transformers arrived on the scene, deep learning hasn’t been the same.

Machine learning is able to generate text essentially indistinguishable from that created by humans

We’ve reached new state–of–the–art performance in many NLP tasks, such as machine translation, question–answering, entailment, named entity recognition, and more

We’ve created multi–modal (text and image) models that can generate amazing art using only a text prompt

We’ve solved a longstanding problem in molecular biology known as protein structure prediction

In this course, you will learn very practical skills for applying transformers, and if you want, detailed theory behind how transformers and attention work.

This is different from most other resources, which only cover the former.

The course is split into 3 major parts:

Using Transformers

Fine–Tuning Transformers

Transformers In–Depth

PART 1: Using Transformers

In this section, you will learn how to use transformers which were trained for you. This costs millions of dollars to do, so it’s not something you want to try by yourself!

We’ll see how these prebuilt models can already be used for a wide array of tasks, including:

text classification (e.g. spam detection, sentiment analysis, document categorization)