🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨

We found this interactive website that shows you visually how transformer models work. 🌐📊

Transformer Explainer:
https://poloclub.github.io/transformer-explainer/

#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech

Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚

This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊

It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖

Free public repository on GitHub. 💻✨

https://github.com/dair-ai/Mathematics-for-ML

#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI

Assembling GPT-like LLMs from scratch on PyTorch 🔥

https://github.com/analyticalrohit/llms-from-scratch

📚 10 notebooks. Step-by-step explanation.

🧩 Breaks down the architecture of LLMs into simple parts.

✅ Suitable for beginners.

🛠 Completely hands-on.

#PyTorch #LLM #AI #MachineLearning #DeepLearning #Code

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Learning AI doesn’t need another random tutorial rabbit hole. 🚫🐇

AI-Study-Group is a public GitHub learning journal for builders trying to navigate AI resources across books, courses, videos, tools, models, datasets, papers, and notes. 📚🤖

It helps you make your own learning path by collecting the materials the author used while learning AI, with quick-start recommendations up front and sections you can scan by resource type. 🗺️✨

Key features: 🌟

• TL;DR starting path – points to one book, one LLM video, and the Hugging Face Agents Course 📖🎥
• Books section – lists AI/ML/DL books with short notes on where each one helps 📚
• Courses and videos – collects practical lectures, tutorials, and talks from sources like MIT, NVIDIA, Hugging Face, Karpathy, and 3Blue1Brown 🎓
• Tools and libraries map – groups frameworks, platforms, visualization tools, and Python libraries for builders 🛠️
• Broader study material – includes models, model hubs, articles, papers, datasets, and AI notes 📄

Free public GitHub repo. 🆓

https://github.com/ArturoNereu/AI-Study-Group

#AI #MachineLearning #DeepLearning #GitHub #StudyGroup #TechLearning

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🚀 Create an LLM from Scratch!

I came across a great find from Vizuara — a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. 🧠✨

Most people use ChatGPT.
But only a few actually understand how it works under the hood. ⚙️

This playlist step by step breaks down all the key concepts without overloading with complex explanations.

📚 What you will learn:
→ The architecture of Transformer 🏗️
→ The internal structure of GPT
→ Tokenization and BPE 🧩
→ Attention mechanisms 🔍
→ The process of training an LLM 📈
→ Full implementations in Python 🐍

✅ Suitable for:
• ML engineers
• AI enthusiasts
• Developers entering the GenAI field
• Anyone who is tired of explaining AI as a "black box" 🕵️

If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini — this material is worth watching. 👀

🔗 Link to the playlist:
https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgsLAr8YCgCwhPIJNNtexWu

#LLM #AI #MachineLearning #Python #GenAI #DeepLearning

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🔖 Found a huge database on System Design for GenAI and LLM! 🤖📚

500+ real reviews of GenAI, LLM, and ML systems from OpenAI, Anthropic, Google, Microsoft, Netflix, and dozens of other companies. 🌐🏢

A real find for those who are building AI products or want to understand how market leaders do it. 🚀💡

⛓️ Link to GitHub
https://github.com/themanojdesai/genai-llm-ml-case-studies


#SystemDesign #GenAI #LLM #MachineLearning #AI #Tech

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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
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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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🚀 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
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

Stop studying LLM from random articles and videos that only explain individual pieces of the puzzle.

📚 LLM from Scratch — this is a practical course on PyTorch for those who want to understand the entire path of modern LLMs: from the first Transformer block to RLHF.

Instead of endless theory, here we gather a complete model training chain:

🔹 Pretraining → Finetuning → Alignment in one course
🔹 Transformer from scratch: positional embeddings, self-attention, multi-head attention, MLP, residual connections, LayerNorm, and full Transformer blocks
🔹 Own training loop without Trainer magic: tokenization, batches, cross-entropy, validation loss, text generation
🔹 Modern architecture improvements: RMSNorm, RoPE, SwiGLU, KV Cache, sliding-window attention, and streaming cache
🔹 Full section on alignment: SFT, reward models, PPO-style RLHF, and GRPO with an analysis of how it looks in the training loop in practice

https://github.com/vivekkalyanarangan30/llm_from_scratch

#LLM #PyTorch #MachineLearning #DeepLearning #AI #Transformer

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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
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO