"Dive into Deep Learning" 📘🤖 is an open-source book that forms the mathematical foundation for large language models. 🧠📐
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. 🧮📉🔄
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. 🚀🔗🧠
It contains over 1,000 pages 📖 and provides clear explanations, practical examples, and exercises. ✅📝 Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. 🌐🔍🤖
arxiv.org/pdf/2106.11342 🔗
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. 🧮📉🔄
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. 🚀🔗🧠
It contains over 1,000 pages 📖 and provides clear explanations, practical examples, and exercises. ✅📝 Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. 🌐🔍🤖
arxiv.org/pdf/2106.11342 🔗
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
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🤖 Designing an RAG with search for 10 million documents while minimizing hallucinations 📚
1️⃣ Document ingestion and normalization 📄
Removing duplicates, converting to a single format, extracting metadata, and maintaining versioning. 🔄
2️⃣ Hybrid search (BM25 + vector representations) 🔍
BM25 handles exact keyword matches, while vector search handles semantic relevance. One approach without the other typically suffers from low accuracy at this scale. 📉
3️⃣ Approximate nearest neighbor search + re-ranking ⚖️
Approximate nearest neighbor search quickly retrieves candidates from millions of fragments. Next, a ranking model recalculates relevance through a more rigorous comparison of the query and fragments. 🧠
4️⃣ Trust scoring for sources 🛡️
Each fragment receives an evaluation based on freshness, source reliability, overlap, and consistency with other found results. Data with low trust should not significantly influence the final response. 🚫
5️⃣ Generation with strict context constraints 🚧
The model only operates within the extracted context. Adding knowledge outside the context is prohibited by the pipeline logic. 🚫
6️⃣ Answers with source attribution 📝
Every significant statement must refer to a specific fragment, document, or timestamp. ⏰
7️⃣ Fallback for low search confidence 📉
If the total context confidence falls below a threshold, a response like "not enough data" is returned. 🛑
8️⃣ Continuous quality checks 🧪
Running attack queries, measuring search completeness, testing for hallucinations, and monitoring ranking degradation. 📊
9️⃣ Caching and memory layer 💾
Frequent queries and search chains are cached to reduce latency and computational cost. ⚡
🔟 Observability at all stages 👁️
Tracing the query path, fragment ranking, and the impact of tokens and failure points. 🛠️
🚀 At the scale of 10 million documents, search quality becomes a more critical factor than the choice of generative model.
#RAG #AI #Search #LLM #DataEngineering #Tech
1️⃣ Document ingestion and normalization 📄
Removing duplicates, converting to a single format, extracting metadata, and maintaining versioning. 🔄
2️⃣ Hybrid search (BM25 + vector representations) 🔍
BM25 handles exact keyword matches, while vector search handles semantic relevance. One approach without the other typically suffers from low accuracy at this scale. 📉
3️⃣ Approximate nearest neighbor search + re-ranking ⚖️
Approximate nearest neighbor search quickly retrieves candidates from millions of fragments. Next, a ranking model recalculates relevance through a more rigorous comparison of the query and fragments. 🧠
4️⃣ Trust scoring for sources 🛡️
Each fragment receives an evaluation based on freshness, source reliability, overlap, and consistency with other found results. Data with low trust should not significantly influence the final response. 🚫
5️⃣ Generation with strict context constraints 🚧
The model only operates within the extracted context. Adding knowledge outside the context is prohibited by the pipeline logic. 🚫
6️⃣ Answers with source attribution 📝
Every significant statement must refer to a specific fragment, document, or timestamp. ⏰
7️⃣ Fallback for low search confidence 📉
If the total context confidence falls below a threshold, a response like "not enough data" is returned. 🛑
8️⃣ Continuous quality checks 🧪
Running attack queries, measuring search completeness, testing for hallucinations, and monitoring ranking degradation. 📊
9️⃣ Caching and memory layer 💾
Frequent queries and search chains are cached to reduce latency and computational cost. ⚡
🔟 Observability at all stages 👁️
Tracing the query path, fragment ranking, and the impact of tokens and failure points. 🛠️
🚀 At the scale of 10 million documents, search quality becomes a more critical factor than the choice of generative model.
#RAG #AI #Search #LLM #DataEngineering #Tech
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🚀 Master Binary Classification with Neural Networks! 🧠✨
Ever wondered how to build a neural network from scratch in Python using NumPy? 🐍📊
Binary classification is at the heart of many machine learning applications. 🎯🤖
Our super-detailed guide walks you through the entire process step by step. 📝📚
💡 Dive in and start building your own neural network today! 🏗🔥
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
Ever wondered how to build a neural network from scratch in Python using NumPy? 🐍📊
Binary classification is at the heart of many machine learning applications. 🎯🤖
Our super-detailed guide walks you through the entire process step by step. 📝📚
💡 Dive in and start building your own neural network today! 🏗🔥
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
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🔥 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
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https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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
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❤3🔥3👍2💩1
Forwarded from Machine Learning with Python
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
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
GitHub
GitHub - dair-ai/Mathematics-for-ML: 🧮 A collection of resources to learn mathematics for machine learning
🧮 A collection of resources to learn mathematics for machine learning - dair-ai/Mathematics-for-ML
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🔖 A huge open-source course on AI Engineering from scratch
In the repository, we've collected:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— 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. 🚀
⛓️ 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:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— 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. 🚀
⛓️ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
#AI #MachineLearning #Python #Rust #OpenSource #Tech
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❤6👍1
Transformer implementations for vision, audio, and AI agents 🤖👁️🎵
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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❤4👍2
FREE MIT books on AI and Machine Learning: 📚🤖
1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems ❯ Vol 1: mlsysbook.ai/vol1/assets/do ❯ 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
❯ Part 1 : probml.github.io/pml-book/book1
❯ 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 ❯ Vol 1: mlsysbook.ai/vol1/assets/do ❯ 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
❯ Part 1 : probml.github.io/pml-book/book1
❯ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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❤6
Introduction to Deep RL and DQN
Link: https://www.dailydoseofds.com/rl-course-part-6/
🤖 #DeepRL #DQN #ReinforcementLearning #AI #MachineLearning #DataScience
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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
Link: https://www.dailydoseofds.com/rl-course-part-6/
🤖 #DeepRL #DQN #ReinforcementLearning #AI #MachineLearning #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
❤6