LLMs Foundations: Tokenization and Word Embeddings Models

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LLMs, AI Chatbots, Word Embeddings Models, Tokenization, ChatGPT, NLP, Machine Learning, AI, Generative AI

What you will learn


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Master LLM and AI chatbots foundation through knowing how and why word embeddings models and tokenization work the way they do.

Learn how to build and use word embeddings models for real life applications like question answering

Develop a β€œbasic mini” LLM

Master the mathematics of LLMs foundation in the most simplified and intuitive way

Practically learn how to use Pytorch to build word embeddings models

Add-On Information:

  • Unlock the fundamental building blocks of large language models (LLMs) and power up your understanding of AI chatbots.
  • Dive into the essential preprocessing steps that transform raw text into machine-readable formats, crucial for any NLP task.
  • Explore the ingenious ways words are converted into numerical representations, paving the way for advanced AI understanding.
  • Demystify the mathematical underpinnings of language models without getting lost in complex jargon.
  • Gain hands-on experience crafting your own rudimentary generative AI system from the ground up.
  • Discover how to leverage semantic relationships captured in word embeddings for sophisticated downstream applications.
  • Understand the trade-offs and design choices involved in different tokenization strategies.
  • Grasp the concept of vector spaces and how they encode meaning and context for words.
  • Acquire practical skills in implementing these core concepts using a leading deep learning framework.
  • Develop an intuitive grasp of how LLMs “understand” and generate human-like text by dissecting their initial processing stages.
  • Learn to critically evaluate the output of LLMs by understanding their internal workings at a foundational level.
  • Equip yourself with the knowledge to confidently discuss and contribute to the rapidly evolving field of generative AI.
  • Gain insights into the architectural decisions that make modern LLMs so powerful.
  • Build a solid foundation for further study in advanced NLP techniques and machine learning.
  • Understand the role of context and its representation in language models.
  • PROS:
  • Provides a crucial conceptual bridge between raw text and sophisticated LLMs.
  • Empowers learners with a practical, hands-on understanding of core AI technologies.
  • Focuses on the essential “why” and “how” behind fundamental LLM components.
  • CONS:
  • May require some prior exposure to basic programming concepts.
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