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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CNN vs Vision Transformer β€” The Battle for Computer Vision πŸ‘βš‘οΈ

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

🧠 CNN (Convolutional Neural Network)

Β· Scans the image with filters
Β· Detects local patterns first (edges β†’ textures β†’ shapes)
Β· Builds understanding layer by layer

πŸ”„ Vision Transformer (ViT)

Β· Splits image into patches (like words in a sentence)
Β· Detects global patterns from the start
Β· 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 β€” but both are shaping the future of how machines see.

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

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-03-18 | ⏱️ Read time: 20 min read

Why one model can’t do two jobs

#DataScience #AI #Python
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πŸ“Œ The New Experience of Coding with AI

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2026-03-18 | ⏱️ Read time: 12 min read

The seduction of AI code assistants

#DataScience #AI #Python
πŸ“Œ Why You Should Stop Worrying About AI Taking Data Science Jobs

πŸ—‚ Category: DATA SCIENCE

πŸ•’ 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 πŸ‘Ύ
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πŸ“Œ Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines

πŸ—‚ Category: AGENTIC AI

πŸ•’ 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
πŸ“Œ Linear Regression Is Actually a Projection Problem, Part 1: The Geometric Intuition

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-03-19 | ⏱️ Read time: 14 min read

A visual guide to vectors and projections

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

πŸ—‚ Category: AGENTIC AI

πŸ•’ 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
πŸ“Œ The Basics of Vibe Engineering

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2026-03-19 | ⏱️ Read time: 14 min read

Building products without the coding part

#DataScience #AI #Python
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πŸ“Œ Building Robust Credit Scoring Models (Part 3)

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ 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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πŸ“Œ How to Measure AI Value

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ 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
πŸ“Œ Agentic RAG Failure Modes: Retrieval Thrash, Tool Storms, and Context Bloat (and How to Spot Them Early)

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ 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
πŸ“Œ The Math That’s Killing Your AI Agent

πŸ—‚ Category: AGENTIC AI

πŸ•’ 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
πŸ“Œ Escaping the SQL Jungle

πŸ—‚ Category: DATA SCIENCE

πŸ•’ 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
πŸ“Œ A Gentle Introduction to Nonlinear Constrained Optimization with Piecewise Linear Approximations

πŸ—‚ Category: DATA SCIENCE

πŸ•’ 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 β€” 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.

β›“ GitHub link

tags: #useful

➑ @codeprogrammer
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πŸ“Œ Prompt Caching with the OpenAI API: A Full Hands-On Python tutorial

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ 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
πŸ“Œ Building a Navier-Stokes Solver in Python from Scratch: Simulating Airflow

πŸ—‚ Category: PHYSICS

πŸ•’ 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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πŸ“Œ I Built a Podcast Clipping App in One Weekend Using Vibe Coding

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2026-03-23 | ⏱️ Read time: 12 min read

Rapid prototyping with Replit, AI agents, and minimal manual coding

#DataScience #AI #Python