In this repository, you will find my Jupyter Notebooks for the projects I completed during the course LLM Engineering: Master AI & Large Language Models (LLMs) by Ed Donner on Udemy. This course provides a broad overview of the world of Large Language Models (LLMs) and their applications, equipping participants with practical skills to tackle real-world challenges in AI.
The course spans 8 weeks and is structured to progressively build your expertise in LLM engineering:
- Overview of Artificial Intelligence and Natural Language Processing (NLP).
- Fundamental principles behind Large Language Models.
- Popular LLMs (e.g., GPT, BERT) and their applications.
- Tokenization, embeddings, and transformers.
- Attention mechanisms in LLMs.
- Understanding model architectures like GPT and BERT.
- Basics of training LLMs on custom datasets.
- Fine-tuning pre-trained LLMs for specific tasks.
- Optimization techniques for LLM training.
- Deploying LLMs in production environments.
- Scaling and optimizing LLMs for performance.
- Building APIs for LLM-based applications.
- Crafting effective prompts for language models.
- Techniques for improving model responses.
- Advanced prompt engineering strategies for complex tasks.
- Implementing LLMs in chatbots, summarization, and translation.
- Custom AI tools powered by LLMs.
- Addressing ethical concerns in LLM deployment.
- Common challenges in LLM engineering.
- Debugging model outputs and performance issues.
- Case studies and solutions for real-world problems.
- Applying course knowledge to build an end-to-end LLM-powered application.
- Presentation and evaluation of the project.
- Preparing for AI industry roles and certifications.
Feel free to use, modify, and share the code and projects in this repository for educational and personal purposes.
However, please give appropriate credit when sharing or referencing this work. Redistribution of substantial parts of the code as part of a commercial product is not permitted without explicit permission.