🚀 Demystifying Activation Functions! 🧠✨
Ever wondered why activation functions are so critical in neural networks? 🤔🤖
They’re the secret sauce that allows models to capture complex, nonlinear relationships! 🔥📈
Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? 🐍📊
Learn more in super-detailed guide: https://lnkd.in/e4CydTtB 🔗📚
#NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI
Ever wondered why activation functions are so critical in neural networks? 🤔🤖
They’re the secret sauce that allows models to capture complex, nonlinear relationships! 🔥📈
Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? 🐍📊
Learn more in super-detailed guide: https://lnkd.in/e4CydTtB 🔗📚
#NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI
❤6🔥2🎉1
"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
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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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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 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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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.
❤10
Forwarded from Machine Learning
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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Forwarded from Data Analytics
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👏1
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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🚀 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
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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🚀 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
❤4
Forwarded from Machine Learning
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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🚀 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
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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🚀 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
❤7
🎓 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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🚀 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
❤2