Unlocking the power of LLMs can feel daunting—but with the right resources, you’ll be fine‑tuning state‑of‑the‑art models and building your own applications in no time. Here are the top five courses, tutorials, and repos to get you there.
A free, hands‑on introduction to transformers and fine‑tuning. You’ll learn about tokenization, attention mechanisms, and how to deploy models in production—all through interactive Jupyter notebooks.
🔗 huggingface.co/learn/nlp-course
Created by Andrew Ng’s team on Coursera, this multi‑part specialization covers prompt engineering, model fine‑tuning, and scaling best practices. Includes quizzes, coding assignments, and real‑world case studies.
🔗 deeplearning.ai/courses/generative-ai-with-large-language-models
Stanford’s flagship NLP course dives deep into the theory and practice of modern architectures—embeddings, transformers, attention, and beyond. All lecture videos, slides, and programming assignments are available for self‑study.
This community‑maintained GitHub repository is a treasure trove of recipes for working with GPT‑style models. From prompt design to fine‑tuning and retrieval‑augmented generation, you’ll find ready‑to‑use code snippets and best practices.
🔗 github.com/openai/openai-cookbook
LangChain is the go‑to framework for building robust LLM applications—chain together prompts, manage conversation memory, and integrate vector databases. Their docs include end‑to‑end examples, quickstarts, and deep‑dives.
Conclusion
Whether you’re a beginner or an experienced practitioner, these five resources cover everything from foundational theory to production‑ready code. Dive in, experiment, and you’ll be building LLM‑powered apps in no time!