Machine Learning
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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.

Admin: @HusseinSheikho || @Hussein_Sheikho
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πŸ“Œ 4 Pandas Concepts That Quietly Break Your Data Pipelines

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-03-23 | ⏱️ Read time: 10 min read

Master data types, index alignment, and defensive Pandas practices to prevent silent bugs in real…

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πŸ“Œ Causal Inference Is Eating Machine Learning

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-03-23 | ⏱️ Read time: 14 min read

Your ML model predicts perfectly but recommends wrong actions. Learn the 5-question diagnostic, method comparison…

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πŸ“Œ Neuro-Symbolic Fraud Detection: Catching Concept Drift Before F1 Drops (Label-Free)

πŸ—‚ Category: DEEP LEARNING

πŸ•’ Date: 2026-03-23 | ⏱️ Read time: 24 min read

This Article asks what happens next. The model has encoded its knowledge of fraud as…

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Forwarded from ML Research Hub
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πŸ’Ύ LLM Architecture Cheat Sheet: from GPT-2 to Trillion-scale Models

LLM Architecture Gallery β€” a page with cards for 39 models (2019–2026): DeepSeek, Qwen, Llama, Kimi, Grok, Nemotron, and others. For each β€” an architecture diagram, decoder type (dense / sparse MoE / hybrid), attention type, and links to technical reports and configs from HuggingFace.

It's clear how the market has converged on MoE + MLA for large models and why hybrid architectures (Mamba-2, DeltaNet, Lightning Attention) are gaining momentum.

πŸ”˜ Open Gallery
https://sebastianraschka.com/llm-architecture-gallery/

https://t.me/DataScienceT πŸ”΄
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πŸ—‚ Cheat sheet on neural networks

It clearly presents all the main types of Neural Networks, with a brief theory and useful tips on Python for working with data and machine learning.

Essentially, it's a compilation of various cheat sheets in one convenient document.

▢️ Link to the cheat sheet
https://www.bigdataheaven.com/wp-content/uploads/2019/02/AI-Neural-Networks.-22.pdf
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πŸ“Œ How to Make Claude Code Improve from its Own Mistakes

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2026-03-24 | ⏱️ Read time: 7 min read

Supercharge Claude Code with continual learning

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πŸ“Œ From Dashboards to Decisions: Rethinking Data & Analytics in the Age of AI

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-03-24 | ⏱️ Read time: 7 min read

How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making

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πŸ“Œ Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2026-03-24 | ⏱️ Read time: 18 min read

We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same…

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πŸ“Œ The Complete Guide to AI Implementation for Chief Data & AI Officers in 2026

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2026-03-24 | ⏱️ Read time: 29 min read

How to leverage a framework to effectively prioritize AI Initiatives to rapidly accelerate growth and…

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