🎓 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/

https://t.iss.one/CodeProgrammer 🌟

If you want to truly understand how AI systems like #GPT, #Claude, #Llama or #Mistral work at their core, these 85 foundational concepts are essential. The visual below breaks down the most important ideas across the full #AI and #LLM landscape.

https://t.iss.one/CodeProgrammer ✅

100+ LLM Interview Questions and Answers (GitHub Repo)

Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.

This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.

🖕 Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub

https://t.iss.one/DataScienceM ✅

🗂 Building our own mini-Skynet — a collection of 10 powerful AI repositories from big tech companies

1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.

2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".

3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.

4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.

5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.

6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.

If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit.

tags: #github #LLM #AI #ML

➡️ https://t.iss.one/CodeProgrammer

🛫 ML Roadmap 2026 — a comprehensive guide to entering ML, LLM, and MLOps

A rather insightful ML roadmap has gone viral on GitHub: within it, the author has compiled a path from a foundation in mathematics, NumPy, and Pandas to LLM, agentic RAG, fine-tuning, MLOps, and interview preparation. The repository indeed includes sections on Karpathy, MCP, RLHF, LoRA/PEFT, and system design for AI interviews.

Conveniently, this isn't just a list of random links, but rather a structured route through the topics:
▶️ Foundations and tools;
▶️ Classic ML;
▶️ LLM and agents;
▶️ Engineering and MLOps;
▶️ Interview preparation.

➡️ GitHub link:
https://github.com/loganthorneloe/ml-roadmap

tags: #ml #llm

➡ https://t.iss.one/CodeProgrammer