Datasets Guide 📚

A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.

Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.

Link: https://docs.unsloth.ai/basics/datasets-guide

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Four best-advanced university courses on NLP & LLM to advance your skills:

1. Advanced NLP -- Carnegie Mellon University
Link: https://lnkd.in/ddEtMghr

2. Recent Advances on Foundation Models -- University of Waterloo
Link: https://lnkd.in/dbdpUV9v

3. Large Language Model Agents -- University of California, Berkeley
Link: https://lnkd.in/d-MdSM8Y

4. Advanced LLM Agent -- University Berkeley
Link: https://lnkd.in/dvCD4HR4

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🎓 Stanford has released a new course: “Transformers & Large Language Models”

The authors are the Amidi brothers, and three free lectures are already available on YouTube. This is probably one of the most systematic introductory courses on modern LLMs.

Course content:

• Transformers: tokenization, embeddings, attention, architecture
• #LLM basics: Mixture of Experts, decoding types
• Training and fine-tuning: SFT, RL, LoRA
• Model evaluation: LLM/VLM-as-a-judge, best practices
• Tricks: RoPE, attention approximations, quantization
• Reasoning: scaling during training and inference
• Agentic approaches: #RAG, tool calling

If you are already familiar with this topic — it’s a great opportunity to refresh your knowledge and try implementing some techniques from scratch.

https://cme295.stanford.edu/syllabus/

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