INTERACTIVE COURSE

Transformers for Natural Language Processing

Intermediate

137 Lessons

15h

Certificate of Completion

AI Explanations

2 Assessments

97 Playgrounds

16 Quizzes

256 Illustrations

Takeaway Skills

Ability to work with Python, PyTorch, and TensorFlow

Ability to explore and implement transformers, as well as key AI language understanding neural network models

Ability to learn the new skills required to become an Industry 4.0 AI Specialist

Proficiency in Python deep learning and the necessary tools needed to effectively enhance language understanding

Ability to create a transformer-based recommendation system

Course Overview

The demand for language understanding is on the rise in many fields, such as media, social media, and research papers. Vast amounts of data need to be processed for research, documents need to be translated and summarized for every area of the economy, and social media posts need to be scanned for ethical and legal reasons, among hundreds of other AI tasks whose use is ever-expanding. This course will cover everything from developing code to prompt design, a new programming skill that controls the behavio...Show More

Course Content

Course Introduction

Getting Started

Basics of Transformers

Introduction: Basics of Transformers

Transformers and Industry 4.0

The History of Transformers Resources to Use Summary: Basics of Transformers Quiz: Basics of Transformers

Architecture of the Transformer Model

Introduction: Architecture of the Transformer Model The Rise of the Transformer: Attention Is All You Need Input Embedding Positional Encoding Sublayer 1: Multi-Head Attention Building Sublayer 1: Multi-Head Attention Post-Layer Normalization and Sublayer 2: Feedforward Network The Decoder Stack and Hugging Face Summary: Architecture of the Transformer Model Quiz: Architecture of the Transformer Model Challenge 1: Build a Multi-Head Attention Sublayer Solution: Build a Multi-Head Attention Sublayer Challenge 2: Evaluate Positional Encodings of a Sentence Solution: Evaluate Positional Encodings of a Sentence

Fine-Tuning BERT Models

Introduction: Fine-Tuning BERT Models Architecture of BERT Preparing the Pretraining Input Environment Pretraining and Setting Up a BERT Model Configuring and Training a BERT Model Evaluating a BERT Model Summary: Fine-Tuning BERT Models Quiz: Fine-Tuning BERT Models

Pretraining a RoBERTa Model from Scratch

Introduction: Pretraining a RoBERTa Model from Scratch Preliminary Steps Involved in Building KantaiBERT Main Steps Involved in Building KantaiBERT Summary: Pretraining a RoBERTa Model from Scratch Quiz: Pretraining a RoBERTa Model from Scratch

Downstream NLP Tasks with Transformers

10 Lessons

Machine Translation with the Transformer

10 Lessons

The Rise of Transformers with GPT-3 Engines

10 Lessons

Applying Transformers for AI Text Summarization

9 Lessons

Matching Tokenizers and Datasets

6 Lessons

Transformers for NLP—Exam 1

Assessment

Semantic Role Labeling with BERT-Based Transformers

7 Lessons

Your Data Speaks: Story, Questions, and Answers

9 Lessons

Detecting Customer Emotions to Make Predictions

7 Lessons

Analyzing Fake News with Transformers

7 Lessons

Interpreting Black Box Transformer Models

8 Lessons

From NLP to Task-Agnostic Transfomer Models

7 Lessons

The Emergence of Transformer-Driven Copilots

8 Lessons

Transformers for NLP—Exam 2

Assessment

Conclusion

1 Lesson

Appendix

4 Lessons

COURSE AUTHOR

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