Learning LLMs
Learning about Large Language Models (LLMs) like GPT-3 or BERT involves several steps, from foundational knowledge in machine learning to more advanced topics specific to LLMs. Here’s a step-by-step guide including resources:
1. Basic Understanding of Machine Learning and Neural Networks
- Resource: Start with online courses like “Machine Learning” by Andrew Ng on Coursera or “Intro to Machine Learning with TensorFlow” on Udacity.
- Books: “Pattern Recognition and Machine Learning” by Christopher Bishop and “Deep Learning” by Goodfellow, Bengio, and Courville.
2. Deep Dive into Deep Learning
- Resource: DeepLearning.AI’s “Deep Learning Specialization” on Coursera.
- Books: “Neural Networks and Deep Learning” by Michael Nielsen (available for free online).
3. Understanding Natural Language Processing (NLP)
- Resource: “Natural Language Processing” specialization by DeepLearning.AI on Coursera.
- Books: “Speech and Language Processing” by Jurafsky and Martin.
4. Learning About Transformers and LLMs
- Resource: “The Illustrated Transformer” by Jay Alammar (blog post).
- Online Course: “Natural Language Processing with Transformers” on Coursera by Hugging Face.
- Books: “Introduction to Transformers” by Hugging Face (available online).
5. Hands-On Experience with Coding
- Tutorials: Follow Python tutorials if you’re not already proficient. Websites like Codecademy, Real Python, or Kaggle are good places to start.
- Practice: Implement basic NLP tasks using libraries like NLTK or spaCy in Python.
6. Explore LLM Frameworks
- Resource: Hugging Face’s Transformers library documentation for practical implementation.
- Hands-On: Work on projects or Kaggle competitions using Transformers.
7. Advanced Topics and Research Papers
- Papers: Read seminal papers like “Attention Is All You Need” (introducing the Transformer model), GPT series, and BERT papers.
- Resource: arXiv.org and the Google Scholar for the latest research.
8. Join the Community
- Forums: Participate in forums like Reddit’s Machine Learning subreddit, Stack Overflow, or join relevant LinkedIn groups.
- Conferences: Follow major conferences like NeurIPS, ICML, or ACL for the latest advancements.
9. Build and Share Projects
- Projects: Build your own projects or contribute to open-source projects.
- GitHub: Share your work on GitHub and collaborate with others.
10. Continuous Learning
- Blogs and Podcasts: Follow AI research blogs (like OpenAI, DeepMind), and listen to podcasts (like Lex Fridman Podcast or AI Alignment).
- Online Communities: Stay active in online communities for continuous learning and networking.
Additional Resources:
- GitHub Repositories: Explore repositories that contain implementations of LLMs.
- Online Seminars and Workshops: Many universities and organizations host seminars that are often free to attend.
Remember, the field of LLMs is rapidly evolving, so staying up-to-date with the latest research and trends is crucial. Also, practical experience is as important as theoretical knowledge, so try to work on real-world projects as you learn.
Here’s a more detailed list of resources with links for learning about Large Language Models (LLMs) and related areas:
Online Courses & Specializations
- Machine Learning by Andrew Ng (Coursera) - Course Link
- Deep Learning Specialization by DeepLearning.AI (Coursera) - Specialization Link
- Natural Language Processing Specialization by DeepLearning.AI (Coursera) - Specialization Link
- Natural Language Processing with Transformers (Coursera, Hugging Face) - Course Link
Books
- “Pattern Recognition and Machine Learning” by Christopher Bishop - Amazon Link
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - Amazon Link
- “Neural Networks and Deep Learning” by Michael Nielsen - Free Online Version
- “Speech and Language Processing” by Dan Jurafsky & James H. Martin - Free Draft Version
Research Papers & Educational Material
- “Attention Is All You Need” (The Transformer Model) - arXiv Link
- Google Scholar for finding the latest research papers - Google Scholar
- “The Illustrated Transformer” by Jay Alammar (Blog Post) - Blog Link
Hands-On Coding and Libraries
- Python Tutorials on Codecademy - Codecademy Python Course
- NLTK Documentation for Natural Language Processing in Python - NLTK Documentation
- Hugging Face Transformers Library - GitHub Repository
Forums and Communities
- Reddit Machine Learning Subreddit - Reddit Link
- Stack Overflow for technical questions - Stack Overflow
Additional Online Resources
- arXiv.org for preprint papers - arXiv Link
- Kaggle for practical competitions and datasets - Kaggle Website
- GitHub for exploring and contributing to projects - GitHub
Podcasts
- Lex Fridman Podcast for AI-related interviews - Podcast Link
Conferences
- NeurIPS (Conference on Neural Information Processing Systems) - Conference Website
- ICML (International Conference on Machine Learning) - Conference Website
Continuous Learning and Updates
- Follow AI research blogs like OpenAI and DeepMind for the latest updates.
- Engage in online communities like LinkedIn groups or Twitter for networking and staying updated.
Each of these resources provides a unique perspective or skill set relevant to understanding and working with LLMs. Balancing theoretical knowledge with practical application is key in this rapidly evolving field.
Notes:
from ChatGPT