Interactive Explainer 🧠✨

The Anatomy of an LLM 🔍
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. ⚙️🧬

🔗 Link: https://www.royvanrijn.com/anatomy-of-an-llm/

#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning

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The only LLM cheat sheet you'll ever need 🚀

Covers the main concepts, architectures, and practical applications.

### Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)

### Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)

### Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)

### Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling)

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#LLM #AI #MachineLearning #DeepLearning #PromptEngineering #Tech

🔖 The Big Book on Fine-Tuning LLMs

A free 115-page book dedicated to the retraining of large language models. 📚

It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. 🚀

#LLM #FineTuning #AI #MachineLearning #DataScience #Tech

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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
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Data Science Interview Questions

💡 Here is your curated list for Data Science interviews!

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🎯 One access, lifetime updates
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#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep

A new collection of free courses has been added:

🔗 https://github.com/dair-ai/ML-Course-Notes

Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. 📚

Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. 🧠

What's inside:

• Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
• A table with lectures, descriptions, videos, notes, and authors
• Links to the original lectures and accompanying notes
• WIP markers for incomplete materials
• Instructions for contributors on adding and improving notes

The idea was appreciated. 👍

Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. 🗺️

#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource

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5 Fun Papers That Explain LLMs Clearly 📚✨

Want to understand LLMs better? Start with these five foundational papers that explain how they work. 🤖

Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. 🧠 But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. 📄 This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. 🔬 In this article, we will go through five papers that explain how LLMs work. So, let's get started. 🚀

More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly

#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech

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If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. 😅

Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. 🤖

Instead of endless Google searches, everything is organized into categories:

• fundamentals of machine learning
• neural networks and modern architectures
• tasks and application areas
• datasets
• libraries and tools
• fairness and AI ethics
• production ML and MLOps

Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. 📝

I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. ⚠️

https://github.com/ZhiningLiu1998/awesome-machine-learning-resources

#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources

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Don't want to read about AI Engineering, but actually want to build something? 🛠️

We've released a collection of 50+ practical tutorials on AI Engineering as open source. 📚

Inside, you'll find step-by-step projects on:

• AI agents and multi-agent systems 🤖
• RAG (Agentic, Vision, and Local) 🔍
• MCP agents ⚙️
• OCR applications 📄
• Voice AI agents 🎙️
• and much more ✨

Everything is free, with source code and ready-made examples. 🥩

https://github.com/Sumanth077/Hands-On-AI-Engineering

#AIEngineering #MachineLearning #OpenSource #AIProjects #TechTutorials #BuildWithAI

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Multi-Label Text Classification with Scikit-LLM 📝

In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. 🚀

Topics we will cover include:

What multi-label classification is and why it matters for nuanced text analysis. 📊
How to set up and configure scikit-LLM with a free, open-source LLM from Groq for zero-shot inference. ⚙️
How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. 📈

Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ 🔗

#ScikitLLM #TextClassification #LLM #MachineLearning #ZeroShot #DataScience

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