Machine Learning
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Real Machine Learning — simple, practical, and built on experience.
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Admin: @HusseinSheikho || @Hussein_Sheikho
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CNN vs Vision Transformer — The Battle for Computer Vision 👁⚡️

Two architectures. One goal: identify the cat. But they see things differently:

🧠 CNN (Convolutional Neural Network)

· Scans the image with filters
· Detects local patterns first (edges → textures → shapes)
· Builds understanding layer by layer

🔄 Vision Transformer (ViT)

· Splits image into patches (like words in a sentence)
· Detects global patterns from the start
· Sees the whole picture using attention mechanisms

Same input. Same output. Different journey.

CNNs think locally and build up.
Transformers think globally from the get-go.

Which one wins? Depends on the task — but both are shaping the future of how machines see.

https://t.me/CodeProgrammer
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📌 Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-03-18 | ⏱️ Read time: 20 min read

Why one model can’t do two jobs

#DataScience #AI #Python
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📌 The New Experience of Coding with AI

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-18 | ⏱️ Read time: 12 min read

The seduction of AI code assistants

#DataScience #AI #Python
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📌 Why You Should Stop Worrying About AI Taking Data Science Jobs

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-18 | ⏱️ Read time: 8 min read

It’s all just fearmongering

#DataScience #AI #Python
PhD Students - Do you need datasets for your research?

Here are 30 datasets for research from NexData.

Use discount code for 20% off: G5W924C3ZI

1. Korean Exam Question Dataset for AI Training

https://lnkd.in/d_paSwt7

2. Multilingual Grammar Correction Dataset

https://lnkd.in/dV43iqTp

3. High quality video caption dataset

https://lnkd.in/dY9kxkhx

4. 3D models and scenes datasets for AI and simulation

https://lnkd.in/dT-zscH4

5. Image editing datasets – object removal, addition & modification

https://lnkd.in/dd8iCGMS

6. QA dataset – visual & text reasoning

https://lnkd.in/dc3TNWFD

7. English instruction tuning dataset

https://lnkd.in/dTeTgd2M

8. Large scale vision language dataset for AI training

https://lnkd.in/dBJuxazN

9. News dataset

https://lnkd.in/dYBJe5gd

10. Global building photos dataset

https://lnkd.in/dVJsDXnC

11. Facial landmarks dataset

https://lnkd.in/dz_KGCS4

12. 3D Human Pose & Landmarks dataset

https://lnkd.in/dXE9ir8Z

13. 3D Hand Pose & Gesture Recognition dataset

https://lnkd.in/d_QdGGb9

14. 14. Driver monitoring dataset – dangerous, fatigue

https://lnkd.in/d6kF-9PW

15. Japanese handwriting OCR dataset

https://lnkd.in/dHnriqrH

16. American English Male voice TTS dataset

https://lnkd.in/dqyvg862

17. Riddles and brain teasers dataset

https://lnkd.in/dKBHY3DE

18. Chinese test questions text

https://lnkd.in/dQpUd8xC

19. Chinese medical question answering data

https://lnkd.in/dsbWUCpz

20. Multi-round interpersonal dialogues text data

https://lnkd.in/dQiUq_Jg

21. Human activity recognition dataset

https://lnkd.in/dHM52MfV

22. Facial expression recognition dataset

https://lnkd.in/dqQAfMau

23. Urban surveillance dataset

https://lnkd.in/dc2RCnTk

24. Human body segmentation dataset

https://lnkd.in/d6sSrDxS

25. Fashion segmentation – clothing & accessories

https://lnkd.in/dptNUTz8

26. Fight video dataset – action recognition

https://lnkd.in/dnY_m5hZ

27. Gesture recognition dataset

https://lnkd.in/dFVPivYg

28. Facial skin defects dataset

https://lnkd.in/dKCbUvU6

29. Smoke detection and behaviour recognition dataset

https://lnkd.in/ddGg56R4

30. Weight loss transformation video dataset

https://lnkd.in/dqqT4ed9

https://t.me/CodeProgrammer 👾
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📌 Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-19 | ⏱️ Read time: 13 min read

A practical guide to caching layers across the RAG pipeline, from query embeddings to full…

#DataScience #AI #Python
📌 Linear Regression Is Actually a Projection Problem, Part 1: The Geometric Intuition

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-19 | ⏱️ Read time: 14 min read

A visual guide to vectors and projections

#DataScience #AI #Python
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📌 Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-19 | ⏱️ Read time: 16 min read

Accelerate coding with AI while staying in control and building reliable, production-ready software.

#DataScience #AI #Python
📌 The Basics of Vibe Engineering

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-19 | ⏱️ Read time: 14 min read

Building products without the coding part

#DataScience #AI #Python
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📌 Building Robust Credit Scoring Models (Part 3)

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-03-20 | ⏱️ Read time: 18 min read

Handling outliers and missing values in borrower data using Python.

#DataScience #AI #Python
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📌 How to Measure AI Value

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-03-20 | ⏱️ Read time: 12 min read

While efficiency is an important source of AI value, it is only part of the…

#DataScience #AI #Python
📌 Agentic RAG Failure Modes: Retrieval Thrash, Tool Storms, and Context Bloat (and How to Spot Them Early)

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-03-20 | ⏱️ Read time: 8 min read

Why agentic RAG systems fail silently in production and how to detect them before your…

#DataScience #AI #Python
📌 The Math That’s Killing Your AI Agent

🗂 Category: AGENTIC AI

🕒 Date: 2026-03-20 | ⏱️ Read time: 12 min read

An 85% accurate AI agent fails 4 out of 5 times on a 10-step task.…

#DataScience #AI #Python
📌 Escaping the SQL Jungle

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-21 | ⏱️ Read time: 13 min read

Most data platforms don’t break overnight; they grow into complexity, query by query. Over time,…

#DataScience #AI #Python
📌 A Gentle Introduction to Nonlinear Constrained Optimization with Piecewise Linear Approximations

🗂 Category: DATA SCIENCE

🕒 Date: 2026-03-21 | ⏱️ Read time: 21 min read

Piecewise linear approximations are a practical way to handle nonlinear constrained models using LP/MIP solvers…

#DataScience #AI #Python
🐍 PyTorch for Beginners: All the Basics on Tensors in One Place

A collection of basic techniques for working with tensors in PyTorch — for those who are starting to get acquainted with the framework and want to quickly master its fundamentals.

What's inside:
▶️ What tensors are and why they are needed

▶️ Tensor initialization: zeros, ones, random, similar size

▶️ Type conversion and switching between NumPy and PyTorch

▶️ Arithmetic, logical operations, tensor comparison

▶️ Matrix multiplication and batch computations

▶️ Broadcasting, view(), reshape(), changing dimensions

▶️ Indexing and slicing: how to access parts of a tensor

▶️ Notebook with code examples
A good starting material to understand the mechanics of tensors before moving on to models and training.

GitHub link

tags: #useful

@codeprogrammer
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📌 Prompt Caching with the OpenAI API: A Full Hands-On Python tutorial

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-03-22 | ⏱️ Read time: 9 min read

A step-by-step guide to making your OpenAI apps faster, cheaper, and more efficient

#DataScience #AI #Python
📌 Building a Navier-Stokes Solver in Python from Scratch: Simulating Airflow

🗂 Category: PHYSICS

🕒 Date: 2026-03-22 | ⏱️ Read time: 6 min read

A hands-on guide to implementing CFD with NumPy, from discretization to airflow simulation around a…

#DataScience #AI #Python
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