LLMs, AI Chatbots, Word Embeddings Models, Tokenization, ChatGPT, NLP, Machine Learning, AI, Generative AI
What you will learn
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 βbasic 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
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- Dive into the foundational building blocks of Large Language Models (LLMs) and AI chatbots, unraveling the intricate processes of tokenization and word embedding.
- Gain a profound understanding of how text is transformed into a numerical format that machines can interpret, exploring the various techniques and algorithms employed in this crucial preprocessing step.
- Explore the concept of vector spaces where words are represented as dense numerical vectors, capturing semantic relationships and contextual nuances.
- Demystify the mathematical underpinnings of these models, presenting complex concepts in an accessible and intuitive manner to build a strong conceptual framework.
- Understand the pivotal role of word embeddings in enabling LLMs to comprehend and generate human-like text, powering applications from search engines to creative writing assistants.
- Discover the evolution of embedding techniques, from simpler methods to more sophisticated contextual embeddings that capture meaning based on surrounding words.
- Learn how to leverage these fundamental concepts to interpret and debug the behavior of advanced LLMs and generative AI systems.
- Acquire hands-on experience with practical implementations, enabling you to build and experiment with your own foundational models.
- Explore the connection between these core concepts and their application in popular AI tools like ChatGPT, gaining insight into their internal workings.
- Develop an appreciation for the computational linguistics principles that drive modern Natural Language Processing (NLP).
- Understand the trade-offs and limitations associated with different tokenization strategies and embedding models.
- Build a solid foundation for further exploration into advanced LLM architectures and their diverse applications.
- PROS:
- Comprehensive understanding of LLM fundamentals beyond superficial usage.
- Practical skills development in model implementation and experimentation.
- Intuitive grasp of complex mathematical concepts.
- CONS:
- May require prior exposure to basic programming concepts for maximum benefit.
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