NVIDIA: Fundamentals of NLP and Transformers

Overview

NVIDIA: Fundamentals of NLP and Transformers Course is the third course of the Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs - Associate Specialization. This course provides learners with foundational knowledge of Natural Language Processing (NLP) and practical skills for working with NLP pipelines and transformer models. It combines theoretical concepts with hands-on exercises to prepare learners for real-world NLP applications. This course covers key NLP topics, including tokenization, text preprocessing techniques, and word embeddings, along with the challenges of handling textual data. Learners will also explore sequence models (RNN, LSTM, GRU) and transformer architectures, gaining practical insights into self-attention mechanisms and encoder-decoder models. The course is structured into two modules, each comprising Lessons and Video Lectures. Learners will engage with approximately 3:00-3:30 hours of video content, covering both theoretical foundations and hands-on practice. Each module includes quizzes to reinforce learning and assess understanding. Course Modules: Module 1: Introduction to NLP: Concepts, Techniques, and Applications Module 2: Sequence Models and Transformers By the end of this course, a learner will be able to: - Understand NLP fundamentals, key tasks, and real-world applications. - Implement NLP techniques, including tokenization, word embeddings, and sequence models. - Explore transformer architecture, self-attention mechanisms, and encoder-decoder models. This course is intended for individuals interested in developing NLP expertise and working with transformer-based models. It is ideal for data scientists, machine learning engineers, and AI specialists seeking hands-on experience in modern NLP techniques.

Syllabus

Introduction to NLP Concepts

Welcome to Week 1 of the NVIDIA: Fundamentals of NLP and Transformers course. This week, we'll cover the basics of NLP, starting with its importance and key tasks. You'll learn about Tokenization, Text Preprocessing, and the challenges of working with text data. We'll also walk through constructing an NLP pipeline, with a demo on NLP Pipeline Classification using a flight dataset, including model fitting and evaluation. Lastly, we'll explore Word Embeddings and compare CBOW and Skipgram. By the end of the week, you'll have a strong foundation in NLP concepts and techniques.

Sequence Models and Transformers

Welcome to Week 2 of the NVIDIA: Fundamentals of NLP and Transformers course. This week, we’ll cover the basics of sequence models, starting with an introduction to RNNs and the challenges of Vanishing and Exploding Gradients. We’ll explore LSTM and GRU architectures and their role in improving RNNs. Next, we’ll dive into Transformers in NLP, focusing on key features of Transformer architecture, Positional Encoding, Self-Attention, and Multi-Head Attention. Finally, we’ll discuss the Encoder-Decoder architecture and different types of Transformer models. By the end of this week, you’ll have a solid understanding of sequence models and Transformers.