๐ How to Improve the Performance of Visual Anomaly Detection Models
๐ Category: COMPUTER VISION
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 6 min read
Apply the best methods from academia to get the most out of practical applications
#DataScience #AI #Python
๐ Category: COMPUTER VISION
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 6 min read
Apply the best methods from academia to get the most out of practical applications
#DataScience #AI #Python
๐ Faster Is Not Always Better: Choosing the Right PostgreSQL Insert Strategy in Python (+Benchmarks)
๐ Category: DATA ENGINEERING
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 6 min read
PostgreSQL is fast. Whether your Python code can or should keep up depends on context.โฆ
#DataScience #AI #Python
๐ Category: DATA ENGINEERING
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 6 min read
PostgreSQL is fast. Whether your Python code can or should keep up depends on context.โฆ
#DataScience #AI #Python
๐ Data Science Spotlight: Selected Problems from Advent of Code 2025
๐ Category: DATA SCIENCE
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 19 min read
Hands-on walkthroughs of problems and solution approaches that power realโworld data science use cases
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 19 min read
Hands-on walkthroughs of problems and solution approaches that power realโworld data science use cases
#DataScience #AI #Python
๐ Mastering Non-Linear Data: A Guide to Scikit-Learnโs SplineTransformer
๐ Category: MACHINE LEARNING
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 7 min read
Forget stiff lines and wild polynomials. Discover why Splines are the โGoldilocksโ of feature engineering,โฆ
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 7 min read
Forget stiff lines and wild polynomials. Discover why Splines are the โGoldilocksโ of feature engineering,โฆ
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๐ Teaching a Neural Network the Mandelbrot Set
๐ Category: MACHINE LEARNING
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 10 min read
And why Fourier features change everything
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 10 min read
And why Fourier features change everything
#DataScience #AI #Python
๐ TDS Newsletter: December Must-Reads on GraphRAG, Data Contracts, and More
๐ Category: THE VARIABLE
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 3 min read
Donโt miss our most popular articles of the previous month
#DataScience #AI #Python
๐ Category: THE VARIABLE
๐ Date: 2026-01-08 | โฑ๏ธ Read time: 3 min read
Donโt miss our most popular articles of the previous month
#DataScience #AI #Python
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๐ Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI
๐ Category: DATA SCIENCE
๐ Date: 2026-01-10 | โฑ๏ธ Read time: 11 min read
A step-by-step journey through data transformation, star schema modeling, and DAX variance analysis with lessonsโฆ
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2026-01-10 | โฑ๏ธ Read time: 11 min read
A step-by-step journey through data transformation, star schema modeling, and DAX variance analysis with lessonsโฆ
#DataScience #AI #Python
๐ How LLMs Handle Infinite Context With Finite Memory
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 10 min read
Achieving infinite context with 114ร less memory
#DataScience #AI #Python
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2026-01-09 | โฑ๏ธ Read time: 10 min read
Achieving infinite context with 114ร less memory
#DataScience #AI #Python
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๐ Federated Learning, Part 1: The Basics of Training Models Where the Data Lives
๐ Category: FEDERATED LEARNING
๐ Date: 2026-01-10 | โฑ๏ธ Read time: 10 min read
Understanding the foundations of federated learning
#DataScience #AI #Python
๐ Category: FEDERATED LEARNING
๐ Date: 2026-01-10 | โฑ๏ธ Read time: 10 min read
Understanding the foundations of federated learning
#DataScience #AI #Python
๐ Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example
๐ Category: AGENTIC AI
๐ Date: 2026-01-11 | โฑ๏ธ Read time: 23 min read
Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomousโฆ
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๐ Category: AGENTIC AI
๐ Date: 2026-01-11 | โฑ๏ธ Read time: 23 min read
Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomousโฆ
#DataScience #AI #Python
4 learning paradigms in machine learning, explained visually:
1. Transfer Learning
2. Fine-tuning
3. Multi-task Learning
4. Federated Learning
๐ @DataScienceM
1. Transfer Learning
2. Fine-tuning
3. Multi-task Learning
4. Federated Learning
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๐ How to Leverage Slash Commands to Code Effectively
๐ Category: LLM APPLICATIONS
๐ Date: 2026-01-11 | โฑ๏ธ Read time: 8 min read
Learn how I utilize slash commands to be a more efficient engineer
#DataScience #AI #Python
๐ Category: LLM APPLICATIONS
๐ Date: 2026-01-11 | โฑ๏ธ Read time: 8 min read
Learn how I utilize slash commands to be a more efficient engineer
#DataScience #AI #Python
Forwarded from Machine Learning with Python
๐_๐๐๐๐ซ๐๐ฌ๐ญ_๐๐๐ข๐ ๐ก๐๐จ๐ซ๐ฌ_๐๐๐โฃ.pdf
2.4 MB
๐ง ๐-๐๐๐๐ซ๐๐ฌ๐ญ ๐๐๐ข๐ ๐ก๐๐จ๐ซ๐ฌ (๐๐๐)โฃ
๐น ๐๐ก๐๐ญ ๐ ๐๐จ๐ฏ๐๐ซ๐๐ ๐ญ๐จ๐๐๐ฒโฃ
๐๐ก๐๐ญ ๐๐๐ ๐ข๐ฌ ๐๐ง๐ ๐ก๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌโฃ
๐๐ข๐๐๐๐ซ๐๐ง๐๐ ๐๐๐ญ๐ฐ๐๐๐ง ๐๐๐ ๐๐จ๐ซ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐ฏ๐ฌ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐งโฃ
๐๐จ๐ฅ๐ ๐จ๐ ๐ (๐ก๐ฒ๐ฉ๐๐ซ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ)โฃ
๐๐ข๐ฌ๐ญ๐๐ง๐๐ ๐ฆ๐๐ญ๐ซ๐ข๐๐ฌ: ๐๐ฎ๐๐ฅ๐ข๐๐๐๐ง ๐ฏ๐ฌ ๐๐๐ง๐ก๐๐ญ๐ญ๐๐งโฃ
๐๐ก๐ฒ ๐๐๐ ๐ข๐ฌ ๐๐๐ฅ๐ฅ๐๐ ๐ ๐ฅ๐๐ณ๐ฒ / ๐ข๐ง๐ฌ๐ญ๐๐ง๐๐-๐๐๐ฌ๐๐ ๐ฅ๐๐๐ซ๐ง๐๐ซโฃ
โฃ
๐ฏ ๐๐จ๐ฉ ๐๐ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ (๐๐ฎ๐ฌ๐ญ-๐๐ง๐จ๐ฐ)โฃ
โฃ
1๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐-๐๐ฆ๐ข๐ณ๐ฆ๐ด๐ต ๐๐ฆ๐ช๐จ๐ฉ๐ฃ๐ฐ๐ณ๐ด (๐๐๐)?โฃ
2๏ธโฃ ๐๐ฉ๐บ ๐ช๐ด ๐๐๐ ๐ค๐ข๐ญ๐ญ๐ฆ๐ฅ ๐ข ๐ญ๐ข๐ป๐บ ๐ญ๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ข๐ญ๐จ๐ฐ๐ณ๐ช๐ต๐ฉ๐ฎ?โฃ
3๏ธโฃ ๐๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ค๐ฆ ๐ฃ๐ฆ๐ต๐ธ๐ฆ๐ฆ๐ฏ ๐๐๐ ๐ค๐ญ๐ข๐ด๐ด๐ช๐ง๐ช๐ค๐ข๐ต๐ช๐ฐ๐ฏ ๐ข๐ฏ๐ฅ ๐๐๐ ๐ณ๐ฆ๐จ๐ณ๐ฆ๐ด๐ด๐ช๐ฐ๐ฏ?โฃ
4๏ธโฃ ๐๐ฐ๐ธ ๐ฅ๐ฐ ๐บ๐ฐ๐ถ ๐ค๐ฉ๐ฐ๐ฐ๐ด๐ฆ ๐ต๐ฉ๐ฆ ๐ท๐ข๐ญ๐ถ๐ฆ ๐ฐ๐ง ๐?โฃ
5๏ธโฃ ๐๐ฉ๐ข๐ต ๐ฉ๐ข๐ฑ๐ฑ๐ฆ๐ฏ๐ด ๐ธ๐ฉ๐ฆ๐ฏ ๐ ๐ช๐ด ๐ต๐ฐ๐ฐ ๐ด๐ฎ๐ข๐ญ๐ญ ๐ฐ๐ณ ๐ต๐ฐ๐ฐ ๐ญ๐ข๐ณ๐จ๐ฆ?โฃ
6๏ธโฃ ๐๐ฉ๐ข๐ต ๐ฅ๐ช๐ด๐ต๐ข๐ฏ๐ค๐ฆ ๐ฎ๐ฆ๐ต๐ณ๐ช๐ค๐ด ๐ข๐ณ๐ฆ ๐ค๐ฐ๐ฎ๐ฎ๐ฐ๐ฏ๐ญ๐บ ๐ถ๐ด๐ฆ๐ฅ ๐ช๐ฏ ๐๐๐?โฃ
7๏ธโฃ ๐๐ฉ๐บ ๐ฅ๐ฐ๐ฆ๐ด ๐๐๐ ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ ๐ฑ๐ฐ๐ฐ๐ณ๐ญ๐บ ๐ฐ๐ฏ ๐ฉ๐ช๐จ๐ฉ-๐ฅ๐ช๐ฎ๐ฆ๐ฏ๐ด๐ช๐ฐ๐ฏ๐ข๐ญ ๐ฅ๐ข๐ต๐ข?โฃ
8๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ต๐ฉ๐ฆ ๐ต๐ช๐ฎ๐ฆ ๐ค๐ฐ๐ฎ๐ฑ๐ญ๐ฆ๐น๐ช๐ต๐บ ๐ฐ๐ง ๐๐๐?โฃ
9๏ธโฃ ๐๐ฐ๐ธ ๐ฅ๐ฐ ๐๐-๐๐ณ๐ฆ๐ฆ ๐ข๐ฏ๐ฅ ๐๐ข๐ญ๐ญ-๐๐ณ๐ฆ๐ฆ ๐ช๐ฎ๐ฑ๐ณ๐ฐ๐ท๐ฆ ๐๐๐ ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ๐ข๐ฏ๐ค๐ฆ?โฃ
๐ ๐๐ฉ๐ฆ๐ฏ ๐ด๐ฉ๐ฐ๐ถ๐ญ๐ฅ ๐บ๐ฐ๐ถ ๐ข๐ท๐ฐ๐ช๐ฅ ๐ถ๐ด๐ช๐ฏ๐จ #๐๐๐?โฃ
https://t.me/CodeProgrammerโญ๏ธ
๐น ๐๐ก๐๐ญ ๐ ๐๐จ๐ฏ๐๐ซ๐๐ ๐ญ๐จ๐๐๐ฒโฃ
๐๐ก๐๐ญ ๐๐๐ ๐ข๐ฌ ๐๐ง๐ ๐ก๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌโฃ
๐๐ข๐๐๐๐ซ๐๐ง๐๐ ๐๐๐ญ๐ฐ๐๐๐ง ๐๐๐ ๐๐จ๐ซ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐ฏ๐ฌ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐งโฃ
๐๐จ๐ฅ๐ ๐จ๐ ๐ (๐ก๐ฒ๐ฉ๐๐ซ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ)โฃ
๐๐ข๐ฌ๐ญ๐๐ง๐๐ ๐ฆ๐๐ญ๐ซ๐ข๐๐ฌ: ๐๐ฎ๐๐ฅ๐ข๐๐๐๐ง ๐ฏ๐ฌ ๐๐๐ง๐ก๐๐ญ๐ญ๐๐งโฃ
๐๐ก๐ฒ ๐๐๐ ๐ข๐ฌ ๐๐๐ฅ๐ฅ๐๐ ๐ ๐ฅ๐๐ณ๐ฒ / ๐ข๐ง๐ฌ๐ญ๐๐ง๐๐-๐๐๐ฌ๐๐ ๐ฅ๐๐๐ซ๐ง๐๐ซโฃ
โฃ
๐ฏ ๐๐จ๐ฉ ๐๐ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ (๐๐ฎ๐ฌ๐ญ-๐๐ง๐จ๐ฐ)โฃ
โฃ
1๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐-๐๐ฆ๐ข๐ณ๐ฆ๐ด๐ต ๐๐ฆ๐ช๐จ๐ฉ๐ฃ๐ฐ๐ณ๐ด (๐๐๐)?โฃ
2๏ธโฃ ๐๐ฉ๐บ ๐ช๐ด ๐๐๐ ๐ค๐ข๐ญ๐ญ๐ฆ๐ฅ ๐ข ๐ญ๐ข๐ป๐บ ๐ญ๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ข๐ญ๐จ๐ฐ๐ณ๐ช๐ต๐ฉ๐ฎ?โฃ
3๏ธโฃ ๐๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ค๐ฆ ๐ฃ๐ฆ๐ต๐ธ๐ฆ๐ฆ๐ฏ ๐๐๐ ๐ค๐ญ๐ข๐ด๐ด๐ช๐ง๐ช๐ค๐ข๐ต๐ช๐ฐ๐ฏ ๐ข๐ฏ๐ฅ ๐๐๐ ๐ณ๐ฆ๐จ๐ณ๐ฆ๐ด๐ด๐ช๐ฐ๐ฏ?โฃ
4๏ธโฃ ๐๐ฐ๐ธ ๐ฅ๐ฐ ๐บ๐ฐ๐ถ ๐ค๐ฉ๐ฐ๐ฐ๐ด๐ฆ ๐ต๐ฉ๐ฆ ๐ท๐ข๐ญ๐ถ๐ฆ ๐ฐ๐ง ๐?โฃ
5๏ธโฃ ๐๐ฉ๐ข๐ต ๐ฉ๐ข๐ฑ๐ฑ๐ฆ๐ฏ๐ด ๐ธ๐ฉ๐ฆ๐ฏ ๐ ๐ช๐ด ๐ต๐ฐ๐ฐ ๐ด๐ฎ๐ข๐ญ๐ญ ๐ฐ๐ณ ๐ต๐ฐ๐ฐ ๐ญ๐ข๐ณ๐จ๐ฆ?โฃ
6๏ธโฃ ๐๐ฉ๐ข๐ต ๐ฅ๐ช๐ด๐ต๐ข๐ฏ๐ค๐ฆ ๐ฎ๐ฆ๐ต๐ณ๐ช๐ค๐ด ๐ข๐ณ๐ฆ ๐ค๐ฐ๐ฎ๐ฎ๐ฐ๐ฏ๐ญ๐บ ๐ถ๐ด๐ฆ๐ฅ ๐ช๐ฏ ๐๐๐?โฃ
7๏ธโฃ ๐๐ฉ๐บ ๐ฅ๐ฐ๐ฆ๐ด ๐๐๐ ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ ๐ฑ๐ฐ๐ฐ๐ณ๐ญ๐บ ๐ฐ๐ฏ ๐ฉ๐ช๐จ๐ฉ-๐ฅ๐ช๐ฎ๐ฆ๐ฏ๐ด๐ช๐ฐ๐ฏ๐ข๐ญ ๐ฅ๐ข๐ต๐ข?โฃ
8๏ธโฃ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ต๐ฉ๐ฆ ๐ต๐ช๐ฎ๐ฆ ๐ค๐ฐ๐ฎ๐ฑ๐ญ๐ฆ๐น๐ช๐ต๐บ ๐ฐ๐ง ๐๐๐?โฃ
9๏ธโฃ ๐๐ฐ๐ธ ๐ฅ๐ฐ ๐๐-๐๐ณ๐ฆ๐ฆ ๐ข๐ฏ๐ฅ ๐๐ข๐ญ๐ญ-๐๐ณ๐ฆ๐ฆ ๐ช๐ฎ๐ฑ๐ณ๐ฐ๐ท๐ฆ ๐๐๐ ๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ๐ข๐ฏ๐ค๐ฆ?โฃ
๐ ๐๐ฉ๐ฆ๐ฏ ๐ด๐ฉ๐ฐ๐ถ๐ญ๐ฅ ๐บ๐ฐ๐ถ ๐ข๐ท๐ฐ๐ช๐ฅ ๐ถ๐ด๐ช๐ฏ๐จ #๐๐๐?โฃ
https://t.me/CodeProgrammer
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