Machine Learning – Telegram
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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📌 Glitches in the Attention Matrix

🗂 Category: DEEP LEARNING

🕒 Date: 2026-01-14 | ⏱️ Read time: 13 min read

A history of Transformer artifacts and the latest research on how to fix them

#DataScience #AI #Python
📌 Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-01-14 | ⏱️ Read time: 15 min read

Seeded topic modeling, integration with LLMs, and training on summarized data are the fresh parts…

#DataScience #AI #Python
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Entry to our VIP channel is completely free today. Tomorrow it will cost $500! 🔥

JOIN 👇

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📌 When Shapley Values Break: A Guide to Robust Model Explainability

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-01-15 | ⏱️ Read time: 9 min read

Shapley Values are one of the most common methods for explainability, yet they can be…

#DataScience #AI #Python
📌 How to Run Coding Agents in Parallel

🗂 Category: AGENTIC AI

🕒 Date: 2026-01-15 | ⏱️ Read time: 8 min read

Get the most out of Claude Code

#DataScience #AI #Python
📌 The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon

🗂 Category: PRODUCTIVITY

🕒 Date: 2026-01-15 | ⏱️ Read time: 8 min read

Designing a centralized system to track daily habits and long-term goals

#DataScience #AI #Python
📌 Do You Smell That? Hidden Technical Debt in AI Development

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-01-15 | ⏱️ Read time: 14 min read

Why speed without standards creates fragile AI products

#DataScience #AI #Python
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📌 Maximum-Effiency Coding Setup

🗂 Category: PROGRAMMING

🕒 Date: 2026-01-16 | ⏱️ Read time: 9 min read

Learn how to be a more efficient programmer

#DataScience #AI #Python
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YOLO Training Template

Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created.

The platform allows:

• to upload your own dataset
• to label manually or auto-label via DINOv3
• to enrich the data if desired
• to train a #YOLO model on your own data
• to run inference immediately
• to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones

All of this is available for free and can already be tested on #GitHub.

Repo:
https://github.com/computer-vision-with-marco/yolo-training-template

https://news.1rj.ru/str/CodeProgrammer
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📌 Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-01-16 | ⏱️ Read time: 18 min read

Why your final LLM layer is OOMing and how to fix it with a custom…

#DataScience #AI #Python
📌 From RGB to Lab: Addressing Color Artifacts in AI Image Compositing

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-01-16 | ⏱️ Read time: 13 min read

A multi-tier approach to segmentation, color correction, and domain-specific enhancement

#DataScience #AI #Python