Learning about Large Language Models (LLMs) involves understanding how they work, how they’re trained, and how to use them effectively. A simple roadmap to get started:
1. Foundations of Machine Learning and NLP
- Learn basic machine learning concepts: supervised vs. unsupervised learning, neural networks, and optimization.
- Study Natural Language Processing (NLP) fundamentals: tokenization, embeddings, attention mechanisms.
2. Deep Dive into Transformers
- Understand the Transformer architecture, which powers most LLMs.
- Key concepts: self-attention, positional encoding, encoder-decoder structure.
- Recommended reading: “Attention Is All You Need” (Vaswani et al., 2017).
3. Training and Fine-Tuning LLMs
- Learn about pre-training on large text corpora and fine-tuning for specific tasks.
- Explore frameworks like PyTorch or TensorFlow for model implementation.
- Study techniques like transfer learning, prompt engineering, and instruction tuning.
4. Practical Tools and Libraries
- Experiment with open-source models such as GPT-Neo, LLaMA, or Mistral.
- Use libraries like Hugging Face Transformers to load and interact with models easily.
5. Ethics and Responsible AI
- Understand bias, fairness, and data privacy concerns.
- Learn about model interpretability and safe deployment practices.
6. Hands-On Projects
- Build a chatbot, summarizer, or text classifier using pre-trained models.
- Try fine-tuning a small model on a custom dataset.
If you’re genuinely trying to understand LLMs and Agentic AI — not just copy prompts
Over the past few weeks, I’ve been revisiting Stanford-led lectures and practical agent resources that do something rare: they explain why things work, not just how to use them.
Part 1: LLMs - from first principles to reasoning
This Stanford lecture series is one of the cleanest step-by-step foundations I’ve seen:
Lecture 1 - Transformer: https://www.youtube.com/watch?v=Ub3GoFaUcds
Lecture 2 - Transformer-Based Models & Tricks: https://www.youtube.com/watch?v=yT84Y5zCnaA
Lecture 3 - Transformers & Large Language Models: https://www.youtube.com/watch?v=Q5baLehv5So
Lecture 4 - LLM Training: https://www.youtube.com/watch?v=VlA_jt_3Qc4
Lecture 5 - LLM tuning: [**https://youtu.be/PmW_TMQ3l0I
Lecture 6 - LLM Reasoning: https://youtu.be/k5Fh-UgTuCo
Lecture 7 - Agentic LLMs: https://www.youtube.com/watch?v=h-7S6HNq0Vg
Lecture 8 - LLM Evaluation: https://www.youtube.com/watch?v=8fNP4N46RRo
Lecture 9 - Recap & Current Trends: https://www.youtube.com/watch?v=Q86qzJ1K1Ss
My advice: treat this like a mini-bootcamp. One lecture at a time. Take notes. Pause. Build something small after each session.
Part 2: AI Agents - fewer videos, deeper understanding
• LLM foundations - https://www.youtube.com/watch?v=zjkBMFhNj_g
• LLMs from scratch - https://www.youtube.com/watch?v=9vM4p9NN0Ts
• Agentic AI overview (Stanford) • https://www.youtube.com/watch?v=kJLiOGle3Lw
Building & evaluating agents https://www.youtube.com/watch?v=d5EltXhbcfA
If you’re learning LLMs and Agentic AI in 2026, this combo gives you a strong, durable foundation — not just short-term hacks.
Build slowly. Understand deeply. Security, reliability, and scale come after clarity.
If you’ve found other resources that actually teach fundamentals, I’d love to learn from you drop them in the comments 👇
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