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 RoPE, Clearly Explained

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2026-01-29 | ⏱️ Read time: 8 min read

Going beyond the math to build intuition

#DataScience #AI #Python
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πŸ“Œ The Unbearable Lightness of Coding

πŸ—‚ Category: LLM APPLICATIONS

πŸ•’ Date: 2026-01-29 | ⏱️ Read time: 9 min read

Confessions of a vibe coder

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πŸ“Œ Randomization Works in Experiments, Even Without Balance

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-29 | ⏱️ Read time: 10 min read

Randomization usually balances confounders in experiments, but what happens when it doesn’t?

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πŸ“Œ Creating an Etch A Sketch App Using Python and Turtle

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2026-01-30 | ⏱️ Read time: 7 min read

A beginner-friendly Python tutorial

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πŸ“Œ Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the β€œBag of Agents”

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2026-01-30 | ⏱️ Read time: 27 min read

Hard-won lessons on how to scale agentic systems without scaling the chaos, including a taxonomy…

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πŸ“Œ On the Possibility of Small Networks for Physics-Informed Learning

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-01-30 | ⏱️ Read time: 20 min read

A new kind of hyperparameter study

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πŸ“Œ Multi-Attribute Decision Matrices, Done Right

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-30 | ⏱️ Read time: 7 min read

How to structure decisions, identify efficient options, and avoid misleading value metrics

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πŸ“Œ How to Run Claude Code for Free with Local and Cloud Models from Ollama

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2026-01-31 | ⏱️ Read time: 16 min read

Ollama now offers Anthropic API compatibility

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πŸ“Œ How to Apply Agentic Coding to Solve Problems

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2026-01-31 | ⏱️ Read time: 7 min read

Learn how to efficiently solve problems with coding agents

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πŸ“Œ Distributed Reinforcement Learning for Scalable High-Performance Policy Optimization

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-02-01 | ⏱️ Read time: 20 min read

Leveraging massive parallelism, asynchronous updates, and multi-machine training to match and exceed human-level performance

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πŸ“Œ Silicon Darwinism: Why Scarcity Is the Source of True Intelligence

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2026-02-02 | ⏱️ Read time: 9 min read

We are confusing β€œsize” with β€œsmart.” The next leap in artificial intelligence will not come…

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πŸ“Œ Building Systems That Survive Real Life

πŸ—‚ Category: AUTHOR SPOTLIGHTS

πŸ•’ Date: 2026-02-02 | ⏱️ Read time: 4 min read

Sara Nobrega on the transition from data science to AI engineering, using LLMs as a…

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πŸ“Œ The Proximity of the Inception Score as an Evaluation Criterion

πŸ—‚ Category: DEEP LEARNING

πŸ•’ Date: 2026-02-03 | ⏱️ Read time: 7 min read

The neighborhood of synthetic data

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πŸ“Œ Routing in a Sparse Graph: a Distributed Q-Learning Approach

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-02-03 | ⏱️ Read time: 10 min read

Distributed agents need only decide one move ahead.

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