AI & ML Papers
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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📚 Become a professional data scientist with these 17 resources!



1️⃣ Python libraries for machine learning

◀️ Introducing the best Python tools and packages for building ML models.



2️⃣ Deep Learning Interactive Book

◀️ Learn deep learning concepts by combining text, math, code, and images.



3️⃣ Anthology of Data Science Learning Resources

◀️ The best courses, books, and tools for learning data science.



4️⃣ Implementing algorithms from scratch

◀️ Coding popular ML algorithms from scratch



5️⃣ Machine Learning Interview Guide

◀️ Fully prepared for job interviews



6️⃣ Real-world machine learning projects

◀️ Learning how to build and deploy models.



7️⃣ Designing machine learning systems

◀️ How to design a scalable and stable ML system.



8️⃣ Machine Learning Mathematics

◀️ Basic mathematical concepts necessary to understand machine learning.



9️⃣ Introduction to Statistical Learning

◀️ Learn algorithms with practical examples.



1️⃣ Machine learning with a probabilistic approach

◀️ Better understanding modeling and uncertainty with a statistical perspective.



1️⃣ UBC Machine Learning

◀️ Deep understanding of machine learning concepts with conceptual teaching from one of the leading professors in the field of ML,



1️⃣ Deep Learning with Andrew Ng

◀️ A strong start in the world of neural networks, CNNs and RNNs.



1️⃣ Linear Algebra with 3Blue1Brown

◀️ Intuitive and visual teaching of linear algebra concepts.



🔴 Machine Learning Course

◀️ A combination of theory and practical training to strengthen ML skills.



1️⃣ Mathematical Optimization with Python

◀️ You will learn the basic concepts of optimization with Python code.



1️⃣ Explainable models in machine learning

◀️ Making complex models understandable.



⚫️ Data Analysis with Python

◀️ Data analysis skills using Pandas and NumPy libraries.


#DataScience #MachineLearning #DeepLearning #Python #AI #MLProjects #DataAnalysis #ExplainableAI #100DaysOfCode #TechEducation #MLInterviewPrep #NeuralNetworks #MathForML #Statistics #Coding #AIForEveryone #PythonForDataScience



⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
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🚀 Master the Transformer Architecture with PyTorch! 🧠

Dive deep into the world of Transformers with this comprehensive PyTorch implementation guide. Whether you're a seasoned ML engineer or just starting out, this resource breaks down the complexities of the Transformer model, inspired by the groundbreaking paper "Attention Is All You Need".

🔗 Check it out here:
https://www.k-a.in/pyt-transformer.html

This guide offers:

🌟 Detailed explanations of each component of the Transformer architecture.

🌟 Step-by-step code implementations in PyTorch.

🌟 Insights into the self-attention mechanism and positional encoding.

By following along, you'll gain a solid understanding of how Transformers work and how to implement them from scratch.

#MachineLearning #DeepLearning #PyTorch #Transformer #AI #NLP #AttentionIsAllYouNeed #Coding #DataScience #NeuralNetworks


💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

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Composer 2 Technical Report

📝 Summary:
Composer 2 is a specialized coding model trained via phased learning for real-world software engineering tasks. It demonstrates superior performance on new and public benchmarks, showcasing strong long-term planning and coding intelligence.

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24477
• PDF: https://arxiv.org/pdf/2603.24477

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For more data science resources:
https://xn--r1a.website/DataScienceT

#AI #Coding #SoftwareEngineering #MachineLearning #CodeGeneration
1
KAT-Coder-V2 Technical Report

📝 Summary:
KAT-Coder-V2 is an agentic coding model that uses a 'Specialize-then-Unify' approach across five expert domains. It employs novel training methods and infrastructure, achieving strong performance on SWE-bench, PinchBench, and other coding benchmarks.

🔹 Publication Date: Published on Mar 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.27703
• PDF: https://arxiv.org/pdf/2603.27703

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For more data science resources:
https://xn--r1a.website/DataScienceT

#AI #Coding #LLM #MachineLearning #Research