Forwarded from Machine Learning with Python
⚡️ All cheat sheets for programmers in one place.
There's a lot of useful stuff inside: short, clear tips on languages, technologies, and frameworks.
No registration required and it's free.
https://overapi.com/
#python #php #Database #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS
https://xn--r1a.website/CodeProgrammer⚡️
There's a lot of useful stuff inside: short, clear tips on languages, technologies, and frameworks.
No registration required and it's free.
https://overapi.com/
#python #php #Database #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS
https://xn--r1a.website/CodeProgrammer
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❤7
Forwarded from Machine Learning with Python
A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications — let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/
tags: #python #deeplearning
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Forwarded from Machine Learning with Python
🗂 A fresh deep learning course from MIT is now publicly available
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
➡️ Link to the course
tags: #Python #DataScience #DeepLearning #AI
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
➡️ Link to the course
tags: #Python #DataScience #DeepLearning #AI
❤6
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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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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❤4
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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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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❤3
🚀 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
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
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❤5
Transformers & LLMs Cheatsheet.pdf
1.4 MB
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
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
❤6
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
📚 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
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 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
❤2
Google has published a free guide on scaling AI models and working with GPUs. 🚀
📘 How to Scale Your Model
https://jax-ml.github.io/scaling-book/
📘 How to Think About GPUs
https://jax-ml.github.io/scaling-book/gpus/
The materials discuss the principles of model scaling, the structure of GPUs, computational limitations, memory bandwidth, parallelism, and other topics that are useful when training and running modern AI models. 💡
It's completely free and available online. 🌐
#AI #MachineLearning #GPU #Scaling #DeepLearning #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
📘 How to Scale Your Model
https://jax-ml.github.io/scaling-book/
📘 How to Think About GPUs
https://jax-ml.github.io/scaling-book/gpus/
The materials discuss the principles of model scaling, the structure of GPUs, computational limitations, memory bandwidth, parallelism, and other topics that are useful when training and running modern AI models. 💡
It's completely free and available online. 🌐
#AI #MachineLearning #GPU #Scaling #DeepLearning #Tech
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 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
How To Scale Your Model
Training LLMs often feels like alchemy, but understanding and optimizing the performance of your models doesn't have to. This book aims to demystify the science of scaling language models: how TPUs (and GPUs) work and how they communicate with each other…
❤1
A large collection of materials on LLM Systems,
• model training (pre-training, RLHF, fault tolerance, stragglers)
• inference and serving
• agent systems
• edge deployment
• multimodal models
• technical reports from major laboratories
• reviews, benchmarks, and leaderboards
• courses on MLSys and collections of articles from conferences
https://github.com/AmberLJC/LLMSys-PaperList
#LLMSys #LLM #MachineLearning #AIResearch #DeepLearning #TechReports
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 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
• model training (pre-training, RLHF, fault tolerance, stragglers)
• inference and serving
• agent systems
• edge deployment
• multimodal models
• technical reports from major laboratories
• reviews, benchmarks, and leaderboards
• courses on MLSys and collections of articles from conferences
https://github.com/AmberLJC/LLMSys-PaperList
#LLMSys #LLM #MachineLearning #AIResearch #DeepLearning #TechReports
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 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
GitHub
GitHub - AmberLJC/LLMSys-PaperList: Large Language Model (LLM) Systems Paper List
Large Language Model (LLM) Systems Paper List. Contribute to AmberLJC/LLMSys-PaperList development by creating an account on GitHub.
❤1
Forwarded from Machine Learning with Python
🎓 A Free AI Course for Beginners by Microsoft
For those just getting into artificial intelligence, Microsoft offers a free course.
It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.
The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.
Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.
➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md
What's your AI level right now?
❤️ — Advanced user
🔥 — Almost zero
#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 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
For those just getting into artificial intelligence, Microsoft offers a free course.
It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.
The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.
Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.
➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md
What's your AI level right now?
❤️ — Advanced user
🔥 — Almost zero
#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 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
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🔖 Building our own GPT-like model in PyTorch
We've found an excellent repository for those who want to understand how modern LLMs are built under the hood.
Inside — 10 Jupyter notebooks with step-by-step explanations and implementations of key components of language models.
GitHub: https://github.com/analyticalrohit/llms-from-scratch
#PyTorch #LLM #MachineLearning #AI #DeepLearning #DataScience
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 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
We've found an excellent repository for those who want to understand how modern LLMs are built under the hood.
Inside — 10 Jupyter notebooks with step-by-step explanations and implementations of key components of language models.
GitHub: https://github.com/analyticalrohit/llms-from-scratch
#PyTorch #LLM #MachineLearning #AI #DeepLearning #DataScience
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 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
❤3