Mastering Large Language Models: A Hands-On Guide to Tokenization and Word Embeddings

What you will learn:

Build a strong foundation in LLMs and AI chatbots by understanding tokenization and word embedding models.
Apply word embedding models to real-world applications such as question answering.
Develop a foundational mini LLM from scratch.
Grasp the mathematics behind LLMs in a simplified and intuitive way.
Use PyTorch to build and train your own word embedding models.
Master different tokenization techniques like WordPiece.
Understand the inner workings of CBOW and Skip-gram models.
Build custom vocabularies and preprocess text data for model training.
Implement and evaluate word embedding models effectively using PyTorch.
Explore advanced topics in LLM development, such as Transformer models.

Description

Dive deep into the fundamental mechanics of Large Language Models (LLMs) and AI chatbots with this comprehensive course. Designed for beginners and seasoned professionals alike, we'll demystify the core concepts of tokenization and word embeddings – the essential building blocks of modern NLP systems. Through engaging video tutorials and practical exercises, you'll gain a profound understanding of how these techniques work, from the underlying mathematics to real-world applications.

This course provides a clear, step-by-step approach, breaking down complex topics into easily digestible lessons. You'll learn to transform raw text into machine-readable units using various tokenization methods, and you'll master the art of representing words as vectors using word embedding models. We'll cover both CBOW and Skip-gram models in detail, guiding you through their implementation in PyTorch and demonstrating their use in tasks like question answering. You'll even build a basic mini-LLM from scratch!

We emphasize a practical, hands-on approach. You'll work through numerous coding exercises, building your own tokenizers and word embedding models, and gain confidence in applying these skills to your own AI projects. Prior knowledge of Python and basic neural networks is helpful, but not strictly required. If you're ready to move beyond the surface level and truly understand how LLMs function, this course is the perfect starting point. Join us and unlock the power of LLMs today!

Curriculum

Introduction

This introductory section sets the stage for the course, providing a brief overview of the key concepts and what you'll learn throughout the program. The 'Introduction' lecture lays the groundwork and provides a roadmap for your learning journey. (Duration: 2:38)

Tokenization

This section delves into the crucial process of tokenization. You'll learn about the WordPiece tokenization algorithm, breaking it down step-by-step, starting with individual characters and progressing to multi-character pairs. You'll gain a solid grasp of score computation and the entire process. The hands-on session allows you to apply your knowledge immediately. (Total duration: ~36 minutes)

Word Embeddings Models

This extensive section explores the world of word embeddings. Starting with an introduction to word embeddings and Word2Vec, you will dive deep into how the CBOW model works, its foundation, training process, and mathematical underpinnings. Interactive quizzes reinforce your learning. You'll then visualize the model's architecture, explore practical embedding lookups, visualize the embedding matrix, and understand post-training usage (inference). Finally, you'll learn about the Skip-gram model and get a grand summary of both CBOW and Skip-gram methods. (Total duration: ~1h 54 mins)

Practical Hands-on: Building, Training, Testing & Using Word Embeddings Models

Put your knowledge into action! This section is packed with practical hands-on exercises using PyTorch. You'll build functions for tokenization, custom vocabulary, data preparation, the CBOW and Skip-gram models, and implement training and testing routines. You'll even build a basic mini question-answering LLM and learn to use pre-trained word embeddings. (Total duration: ~2h 19 mins)

What Next For Your LLM Journey?

This final section provides a glimpse into the future, outlining the next steps in your LLM journey. It briefly introduces Transformer models and their connection to LLMs and AI chatbots, paving the way for further exploration. (Total duration: ~4 minutes)

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