Getting Started with LLMs/AI/ML

The best way to learn LLMs (Large Language Models) depends on your existing knowledge and goals. Here's a roadmap to consider:

Beginners:

1. Grasp the fundamentals:
   • Understand basic machine learning concepts. Resources like https://developers.google.com/machine-learning/crash-course can help.
   • Learn about transformers, the backbone of most LLMs. Check out the simplified explanation I provided earlier.
2. Explore pre-trained models:
   • You don't need to build LLMs from scratch. Platforms like TensorFlow or PyTorch offer pre-trained models you can use for specific tasks.
   • Look into tutorials on using these libraries for tasks like sentiment analysis or question answering.
3. Playgrounds and Tutorials:
   • Platforms like Hugging Face offer playgrounds where you can experiment with pre-trained transformers (https://huggingface.co/Playground).
   • Explore online tutorials like the PyTorch Transformers Tutorial (https://huggingface.co/docs/transformers/index) or video explanations like [YouTube video transformers neural network ON youtube.com] (Illustrated Guide to Transformers).

Intermediate Learners:

1. Deepen your understanding:
   • Take online courses or read research papers on LLMs. Look for courses on platforms like Coursera or Udacity that delve deeper into LLM architecture and functionalities.
2. Practice with coding:
   • Learn Python programming, as most LLM frameworks use it.
   • Once comfortable, explore building simple LLM projects using libraries like TensorFlow or PyTorch.
3. Engage with the community:
   • Join online forums or communities like the Hugging Face forum (https://huggingface.co/docs/transformers/v4.17.0/en/tasks/language_modeling) to discuss LLMs with others and learn from their experiences.

Advanced Learners:

1. Advanced techniques:
   • Explore fine-tuning pre-trained LLMs for specialized tasks.
   • Learn about prompt engineering, a technique for crafting prompts that guide the LLM towards desired outputs.
2. Research and development:
   • If you have a strong foundation, consider delving into research papers on cutting-edge LLM advancements.
   • You could even contribute to open-source LLM projects on platforms like GitHub.