📌 Why Human-Centered Data Analytics Matters More Than Ever

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-14 | ⏱️ Read time: 8 min read

From optimizing metrics to designing meaning: putting people back into data-driven decisions

#DataScience #AI #Python

📌 What Is a Knowledge Graph — and Why It Matters

🗂 Category: DATA SCIENCE

🕒 Date: 2026-01-14 | ⏱️ Read time: 18 min read

How structured knowledge became healthcare’s quiet advantage

#DataScience #AI #Python

📌 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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📌 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…

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📌 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

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📌 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

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📌 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

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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

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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

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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

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📌 The Great Data Closure: Why Databricks and Snowflake Are Hitting Their Ceiling

🗂 Category: DATA SCIENCE

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

Acquisitions, venture, and an increasingly competitive landscape all point to a market ceiling

#DataScience #AI #Python

🤖 Machine Learning Tutorials Repository

1. Python
2. Computer Vision: Techniques, algorithms
3. NLP
4. Matplotlib
5. NumPy
6. Pandas
7. MLOps
8. LLMs
9. PyTorch/TensorFlow

git clone https://github.com/patchy631/machine-learning

🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main

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