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

  1. Machine Learning by Andrew Ng (Coursera) - Course Link
  2. Deep Learning Specialization by DeepLearning.AI (Coursera) - Specialization Link
  3. Natural Language Processing Specialization by DeepLearning.AI (Coursera) - Specialization Link
  4. Natural Language Processing with Transformers (Coursera, Hugging Face) - Course Link

Books

  1. “Pattern Recognition and Machine Learning” by Christopher Bishop - Amazon Link
  2. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - Amazon Link
  3. “Neural Networks and Deep Learning” by Michael Nielsen - Free Online Version
  4. “Speech and Language Processing” by Dan Jurafsky & James H. Martin - Free Draft Version

Research Papers & Educational Material

  1. “Attention Is All You Need” (The Transformer Model) - arXiv Link
  2. Google Scholar for finding the latest research papers - Google Scholar
  3. “The Illustrated Transformer” by Jay Alammar (Blog Post) - Blog Link

Hands-On Coding and Libraries

  1. Python Tutorials on Codecademy - Codecademy Python Course
  2. NLTK Documentation for Natural Language Processing in Python - NLTK Documentation
  3. Hugging Face Transformers Library - GitHub Repository

Forums and Communities

  1. Reddit Machine Learning Subreddit - Reddit Link
  2. Stack Overflow for technical questions - Stack Overflow

Additional Online Resources

  1. arXiv.org for preprint papers - arXiv Link
  2. Kaggle for practical competitions and datasets - Kaggle Website
  3. GitHub for exploring and contributing to projects - GitHub

Podcasts

  1. Lex Fridman Podcast for AI-related interviews - Podcast Link

Conferences

  1. NeurIPS (Conference on Neural Information Processing Systems) - Conference Website
  2. 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