🚀Stanford just completed a must-watch for anyone serious about AI:

🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold.

If you’re building your AI career, stop scrolling.
This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.

📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
• How Transformers actually work (tokenization, attention, embeddings)
• Decoding strategies & MoEs
• LLM finetuning (LoRA, RLHF, supervised)
• Evaluation techniques (LLM-as-a-judge)
• Optimization tricks (RoPE, quantization, approximations)
• Reasoning & scaling
• Agentic workflows (RAG, tool calling)

🧠 My workflow: I usually take the transcripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.

🎥 Watch these now:

- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ

🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.

If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss.

Let’s level up.
https://t.iss.one/CodeProgrammer 😅

Automatic translator in Python!

We translate a text in a few lines using deep-translator. It supports dozens of languages: from English and Russian to Japanese and Arabic.

Install the library:

pip install deep-translator

Example of use:

from deep_translator import GoogleTranslator

text = "Hello, how are you?"
result = GoogleTranslator(source="ru", target="en").translate(text)

print("Original:", text)
print("Translation:", result)

Mass translation of a list:

texts = ["Hello", "What's your name?", "See you later"]
for t in texts:
    print("→", GoogleTranslator(source="ru", target="es").translate(t))

🔥 We get a mini-Google Translate right in Python: you can embed it in a chatbot, use it in notes, or automate work with the API.

🚪 @DataScience4

In scientific work, the most time is spent on reading articles, data, and reports.

On GitHub, there is a collection called Awesome AI for Science -»»» a catalog of AI tools for all stages of research.

Inside:

-» working with literature
-» data analysis
-» turning articles into posters
-» automating experiments
-» tools for biology, chemistry, physics, and other fields

GitHub: https://github.com/ai-boost/awesome-ai-for-science

The list includes Paper2Poster, MinerU, The AI Scientist, as well as articles, datasets, and frameworks.
In fact, this is a complete set of tools for AI support in scientific research.

👉 https://t.iss.one/CodeProgrammer

AI-ML Roadmap from Scratch

👉 https://github.com/aadi1011/AI-ML-Roadmap-from-scratch?tab=readme-ov-file

https://t.iss.one/CodeProgrammer 🌟

Like and Share