Time Complexity of 10 Most Popular ML Algorithms Know What You're Waiting For ⏳🧠
https://t.me/DataScienceM
https://t.me/DataScienceM
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📌 How to Effectively Review Claude Code Output
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-17 | ⏱️ Read time: 7 min read
Get more out of your coding agents by making reviewing more efficient
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-17 | ⏱️ Read time: 7 min read
Get more out of your coding agents by making reviewing more efficient
#DataScience #AI #Python
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📌 Self-Hosting Your First LLM
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-17 | ⏱️ Read time: 20 min read
Privacy. Cost. Customization. Everything you need to know—step by step.
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-17 | ⏱️ Read time: 20 min read
Privacy. Cost. Customization. Everything you need to know—step by step.
#DataScience #AI #Python
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
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
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-03-18 | ⏱️ Read time: 20 min read
Why one model can’t do two jobs
#DataScience #AI #Python
Forwarded from Machine Learning with 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
🗂 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
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-18 | ⏱️ Read time: 8 min read
It’s all just fearmongering
#DataScience #AI #Python
Forwarded from Machine Learning with 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👾
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
🗂 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
🗂 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
🗂 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
🗂 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
🗂 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
🗂 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
🗂 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
🗂 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
🗂 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
🗂 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
Forwarded from Machine Learning with Python
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:
A good starting material to understand the mechanics of tensors before moving on to models and training.▶️ 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
tags: #useful
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