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 Simplifying the Python Code for Data Engineering Projects

πŸ—‚ Category: DATA ENGINEERING

πŸ•’ Date: 2024-06-12 | ⏱️ Read time: 12 min read

Python tricks and techniques for data ingestion, validation, processing, and testing: a practical walkthrough
πŸ“Œ Model Interpretability Using Credit Card Fraud Data

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-12 | ⏱️ Read time: 20 min read

Why model interpretability is important
πŸ“Œ How to Pivot Tables in SQL

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-12 | ⏱️ Read time: 12 min read

A comprehensive guide to creating pivot tables in SQL for enhanced data analysis
πŸ“Œ The Math Behind KAN – Kolmogorov-Arnold Networks

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-12 | ⏱️ Read time: 15 min read

A new alternative to the classic Multi-Layer Perceptron is out. Why is it more accurate…
πŸ“Œ Spatial Index: Grid Systems

πŸ—‚ Category: DATABASE DESIGN

πŸ•’ Date: 2024-06-12 | ⏱️ Read time: 12 min read

Grid Systems in Spatial Indexing using GeoHash and Google S2
πŸ“Œ Deep Learning Illustrated, Part 4: Recurrent Neural Networks

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 17 min read

An illustrated and intuitive guide on the inner workings of an RNN and the Softmax…
πŸ“Œ ASA’s Caution: Rethinking How We Use p-Values in Research

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 9 min read

Understanding the ASA’s statement to enhance your data science practices
πŸ“Œ MLOps – Data Validation with PyTest

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 12 min read

Run deterministic and non-deterministic tests to validate your dataset
πŸ“Œ An Open Data-Driven Approach to Optimising Healthcare Facility Locations Using Python

πŸ—‚ Category:

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 15 min read

A tutorial in Python with an open data stack
πŸ“Œ Key Roles in a Fraud Prediction project with Machine Learning

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 6 min read

What type of roles are involved in developing a ML model for fraud prediction?
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πŸ“Œ How to Maximize Your Impact as a Data Scientist

πŸ—‚ Category: ANALYTICS

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 13 min read

Actionable advice to accelerate your career
πŸ“Œ Multi-Head Attention – Formally Explained and Defined

πŸ—‚ Category: DEEP LEARNING

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 10 min read

A comprehensive and detailed formalization of multi-head attention.
πŸ“Œ Beyond FOMO – Keeping up to date in AI

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 9 min read

Don’t get stressed but enjoy the journey.
πŸ“Œ Optimize Production with Rβ€Š-β€ŠPart I

πŸ—‚ Category:

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 8 min read

An introduction to linear programming with R
πŸ“Œ How I Built an LLM-Based Game from Scratch

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 17 min read

Part I: Game concepts and Causal Graphs for LLMs
πŸ€–πŸ§  NVIDIA, MIT, HKU and Tsinghua University Introduce QeRL: A Powerful Quantum Leap in Reinforcement Learning for LLMs

πŸ—“οΈ 17 Oct 2025
πŸ“š AI News & Trends

The rise of large language models (LLMs) has redefined artificial intelligence powering everything from conversational AI to autonomous reasoning systems. However, training these models especially through reinforcement learning (RL) is computationally expensive requiring massive GPU resources and long training cycles. To address this, a team of researchers from NVIDIA, Massachusetts Institute of Technology (MIT), The ...

#QuantumLearning #ReinforcementLearning #LLMs #NVIDIA #MIT #TsinghuaUniversity
πŸ“Œ SQL Explained: Ranking Analytics

πŸ—‚ Category: DATA ENGINEERING

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 11 min read

What they are and how you use them
πŸ“Œ Anatomy of Windows Functions

πŸ—‚ Category: DATA ENGINEERING

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 15 min read

Theory and practice of an underappreciated SQL operation
πŸ“Œ Reinforcement Learning, Part 4: Monte Carlo Control

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 17 min read

Harnessing Monte Carlo algorithms to discover the best strategies
πŸ“Œ Spatial Index: Space-Filling Curves

πŸ—‚ Category: DATABASE DESIGN

πŸ•’ Date: 2024-06-11 | ⏱️ Read time: 11 min read

Spatial Index and Space Filling Curves for Multi-dimensional data
πŸ€–πŸ§  Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents

πŸ—“οΈ 17 Oct 2025
πŸ“š AI News & Trends

AEPO (Agentic Entropy-Balanced Policy Optimization) represents a major advancement in the evolution of Agentic Reinforcement Learning (RL). As large language models (LLMs) increasingly act as autonomous web agents – searching, reasoning and interacting with tools – the need for balanced exploration and stability has become crucial. Traditional RL methods often rely heavily on entropy to ...

#AgenticRL #ReinforcementLearning #LLMs #WebAgents #EntropyBalanced #PolicyOptimization