با عرض سلام اکانت @Raminmousa به دلیل جابجایی در زمان لاگین تلگرام تعلیق شده و در تلاش برای رفع این مشکلیم. دوستانی که باهام پروژه دارن لطفا با این ایدیم در ارتباط باشن
@Raminmousa1
@Raminmousa1
❤5
Machine learning books and papers pinned «با عرض سلام اکانت @Raminmousa به دلیل جابجایی در زمان لاگین تلگرام تعلیق شده و در تلاش برای رفع این مشکلیم. دوستانی که باهام پروژه دارن لطفا با این ایدیم در ارتباط باشن @Raminmousa1»
🔥 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/
@Machine_learn
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
@Machine_learn
poloclub.github.io
Transformer Explainer: LLM Transformer Model Visually Explained
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
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Forwarded from Machine learning books and papers
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
Read
@Machine_learn
Read
@Machine_learn
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🔖 Comprehensive Practical Course on Reinforcement Learning
We've found a repository that will help you learn Reinforcement Learning, from basic concepts to advanced algorithms.
The author supports the theory with practical examples using TensorFlow, making the material ideal for self-study.
⛓️ Link to GitHub
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
@Machine_learn
We've found a repository that will help you learn Reinforcement Learning, from basic concepts to advanced algorithms.
The author supports the theory with practical examples using TensorFlow, making the material ideal for self-study.
⛓️ Link to GitHub
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
@Machine_learn
10 GitHub repositories that are worth checking out for an AI engineer 🤖
1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
👉 https://github.com/mlabonne/llm-course
@Machine_learn
1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
👉 https://github.com/mlabonne/llm-course
@Machine_learn
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Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers
🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.
📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.
📖 It contains 20 chapters:
* Vectors, matrices, calculus
* Statistics and probability
* Machine learning and deep learning
* NLP, computer vision, audio/speech
* Multimodal learning and autonomous systems
* GNN, OS, algorithms
* Production engineering, GPU/SIMD
* AI inference, ML systems design, and applied AI
💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI.
🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium
@Machine_learn
🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.
📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.
📖 It contains 20 chapters:
* Vectors, matrices, calculus
* Statistics and probability
* Machine learning and deep learning
* NLP, computer vision, audio/speech
* Multimodal learning and autonomous systems
* GNN, OS, algorithms
* Production engineering, GPU/SIMD
* AI inference, ML systems design, and applied AI
💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI.
🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium
@Machine_learn
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🔖 One of the most useful books on Agentic AI
This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents.
It's an excellent option to get a holistic picture and understand which topics deserve deeper study.
⛓️ Link to the book
https://arxiv.org/abs/2606.24937
@Machine_learn
This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents.
It's an excellent option to get a holistic picture and understand which topics deserve deeper study.
⛓️ Link to the book
https://arxiv.org/abs/2606.24937
@Machine_learn
❤3
با عرض سلام سه موضوع زیر جهت نگارش مقالات مدنظر داریم. که در هر سه مقاله به دو جایگاه نیاز داریم. مقالات کاملا مشارکتی هست و علاوه بر تقبل هزینه کار نیز باید انجام بشه.
1: Survey on knowledge graph and large language models
_ auth2: 300$
_auth3:200$
2: Survey on challenges of large language models
_ auth2: 300$
_auth3:200$
3: New learning model for skin cancer detection
_ auth2: 300$
_auth3:200$
جهت مشارکت میتونین با ایدی بنده در ارتباط باشین. زمان شروع هر مقاله یک هفته بعد از تشکیل تیم.
@Raminmousa1
@Machine_learn
1: Survey on knowledge graph and large language models
_ auth2: 300$
_auth3:200$
2: Survey on challenges of large language models
_ auth2: 300$
_auth3:200$
3: New learning model for skin cancer detection
_ auth2: 300$
_auth3:200$
جهت مشارکت میتونین با ایدی بنده در ارتباط باشین. زمان شروع هر مقاله یک هفته بعد از تشکیل تیم.
@Raminmousa1
@Machine_learn
❤3
Machine learning books and papers pinned «با عرض سلام سه موضوع زیر جهت نگارش مقالات مدنظر داریم. که در هر سه مقاله به دو جایگاه نیاز داریم. مقالات کاملا مشارکتی هست و علاوه بر تقبل هزینه کار نیز باید انجام بشه. 1: Survey on knowledge graph and large language models _ auth2: 300$ _auth3:200$ 2: Survey…»
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Attention Heatmap vs Token Pruning 🔍✂️
🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms
#AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM
@Machine_learn
🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms
#AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM
@Machine_learn
❤1
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🔖 Learning Data Science through interactive examples
One of the most useful repositories for those who want to better understand machine learning.
It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.
⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython
@Machine_learn
One of the most useful repositories for those who want to better understand machine learning.
It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.
⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython
@Machine_learn
❤3
report2 (1).pdf
322.3 KB
هر هفته با یک موضوع تحقیقی
موضوع :تولید داده های سری زمانی با استفاده از شبکه های عصبی تخاصمی در شبکه های هوشمند
#Thesis #proposed_research
@Raminmousa1
@Machine_learn
موضوع :تولید داده های سری زمانی با استفاده از شبکه های عصبی تخاصمی در شبکه های هوشمند
#Thesis #proposed_research
@Raminmousa1
@Machine_learn
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