๐ The Machine Learning โAdvent Calendarโ Day 21: Gradient Boosted Decision Tree Regressor in Excel
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-22 | โฑ๏ธ Read time: 10 min read
Gradient descent in function space with decision trees
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-22 | โฑ๏ธ Read time: 10 min read
Gradient descent in function space with decision trees
#DataScience #AI #Python
โค2๐1
๐ The Machine Learning โAdvent Calendarโ Day 20: Gradient Boosted Linear Regression in Excel
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-22 | โฑ๏ธ Read time: 10 min read
From Random Ensembles to Optimization: Gradient Boosting Explained
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-22 | โฑ๏ธ Read time: 10 min read
From Random Ensembles to Optimization: Gradient Boosting Explained
#DataScience #AI #Python
โค2
๐ ChatLLM Presents a Streamlined Solution to Addressing the Real Bottleneck in AI
๐ Category: SPONSORED CONTENT
๐ Date: 2025-12-22 | โฑ๏ธ Read time: 8 min read
For the last couple of years, a lot of the conversation around AI has revolvedโฆ
#DataScience #AI #Python
๐ Category: SPONSORED CONTENT
๐ Date: 2025-12-22 | โฑ๏ธ Read time: 8 min read
For the last couple of years, a lot of the conversation around AI has revolvedโฆ
#DataScience #AI #Python
โค2
๐ The Machine Learning โAdvent Calendarโ Day 23: CNN in Excel
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-23 | โฑ๏ธ Read time: 8 min read
A step-by-step 1D CNN for text, built in Excel, where every filter, weight, and decisionโฆ
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-23 | โฑ๏ธ Read time: 8 min read
A step-by-step 1D CNN for text, built in Excel, where every filter, weight, and decisionโฆ
#DataScience #AI #Python
๐ How Agents Plan Tasks with To-Do Lists
๐ Category: AGENTIC AI
๐ Date: 2025-12-23 | โฑ๏ธ Read time: 7 min read
Understanding the process behind agentic planning and task management in LangChain
#DataScience #AI #Python
๐ Category: AGENTIC AI
๐ Date: 2025-12-23 | โฑ๏ธ Read time: 7 min read
Understanding the process behind agentic planning and task management in LangChain
#DataScience #AI #Python
โค2๐ฅ1
๐ Stop Retraining Blindly: Use PSI to Build a Smarter Monitoring Pipeline
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-23 | โฑ๏ธ Read time: 6 min read
A data scientistโs guide to population stability index (PSI)
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-23 | โฑ๏ธ Read time: 6 min read
A data scientistโs guide to population stability index (PSI)
#DataScience #AI #Python
๐ฅ1
๐ The Machine Learning โAdvent Calendarโ Day 24: Transformers for Text in Excel
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-24 | โฑ๏ธ Read time: 10 min read
An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings intoโฆ
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-24 | โฑ๏ธ Read time: 10 min read
An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings intoโฆ
#DataScience #AI #Python
โค1๐1
๐ Is Your Model Time-Blind? The Case for Cyclical Feature Encoding
๐ Category: DATA SCIENCE
๐ Date: 2025-12-24 | โฑ๏ธ Read time: 7 min read
How cyclical encoding improves machine learning prediction
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2025-12-24 | โฑ๏ธ Read time: 7 min read
How cyclical encoding improves machine learning prediction
#DataScience #AI #Python
๐ 4 Techniques to Optimize AI Coding Efficiency
๐ Category: PROGRAMMING
๐ Date: 2025-12-24 | โฑ๏ธ Read time: 8 min read
Learn how to code more effectively using AI
#DataScience #AI #Python
๐ Category: PROGRAMMING
๐ Date: 2025-12-24 | โฑ๏ธ Read time: 8 min read
Learn how to code more effectively using AI
#DataScience #AI #Python
โค3
๐ Bonferroni vs. Benjamini-Hochberg: Choosing Your P-Value Correction
๐ Category: STATISTICS
๐ Date: 2025-12-24 | โฑ๏ธ Read time: 11 min read
Multiple hypothesis testing, P-values, and Monte Carlo
#DataScience #AI #Python
๐ Category: STATISTICS
๐ Date: 2025-12-24 | โฑ๏ธ Read time: 11 min read
Multiple hypothesis testing, P-values, and Monte Carlo
#DataScience #AI #Python
โค3
๐ Keeping Probabilities Honest: The Jacobian Adjustment
๐ Category: DATA SCIENCE
๐ Date: 2025-12-25 | โฑ๏ธ Read time: 10 min read
An intuitive explanation of transforming random variables correctly.
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2025-12-25 | โฑ๏ธ Read time: 10 min read
An intuitive explanation of transforming random variables correctly.
#DataScience #AI #Python
โค1
๐ Why MAP and MRR Fail for Search Ranking (and What to Use Instead)
๐ Category: DATA SCIENCE
๐ Date: 2025-12-25 | โฑ๏ธ Read time: 9 min read
MAP and MRR look intuitive, but they quietly break ranking evaluation. Hereโs why these metricsโฆ
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2025-12-25 | โฑ๏ธ Read time: 9 min read
MAP and MRR look intuitive, but they quietly break ranking evaluation. Hereโs why these metricsโฆ
#DataScience #AI #Python
โค1
Forwarded from AI & ML Papers
ML Engineers: NVIDIA has released a guide for beginners on fine-tuning LLMs using Unsloth.
The guide covers:
- training methods: LoRA, FFT, RL
- when and why to do fine-tuning, real use cases
- how much data and VRAM are required
- how to train locally on DGX Spark, RTX graphics cards, and more
Guide: https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/
๐ https://t.me/DataScienceT
The guide covers:
- training methods: LoRA, FFT, RL
- when and why to do fine-tuning, real use cases
- how much data and VRAM are required
- how to train locally on DGX Spark, RTX graphics cards, and more
Guide: https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/
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โค4๐1
๐ Think Your Python Code Is Slow? Stop Guessing and Start Measuring
๐ Category: PROGRAMMING
๐ Date: 2025-12-26 | โฑ๏ธ Read time: 13 min read
A hands-on tour of using cProfile + SnakeViz to find (and fix) the โhotโ pathsโฆ
#DataScience #AI #Python
๐ Category: PROGRAMMING
๐ Date: 2025-12-26 | โฑ๏ธ Read time: 13 min read
A hands-on tour of using cProfile + SnakeViz to find (and fix) the โhotโ pathsโฆ
#DataScience #AI #Python
โค3
๐ How to Build an AI-Powered Weather ETL Pipeline with Databricks and GPT-4o: From API To Dashboard
๐ Category: DATA ENGINEERING
๐ Date: 2025-12-26 | โฑ๏ธ Read time: 11 min read
A step-by-step guide from weather API ETL to dashboard on Databricks
#DataScience #AI #Python
๐ Category: DATA ENGINEERING
๐ Date: 2025-12-26 | โฑ๏ธ Read time: 11 min read
A step-by-step guide from weather API ETL to dashboard on Databricks
#DataScience #AI #Python
โค2
Mcp_Chapter_11.pdf
1.2 MB
โค3๐2
Forwarded from Machine Learning with Python
transformer Q&A.pdf
1.3 MB
๐๐๐ซ๐โ๐ฌ ๐ ๐ช๐ฎ๐ข๐๐ค ๐๐ซ๐๐๐ค๐๐จ๐ฐ๐ง ๐๐ซ๐จ๐ฆ ๐ญ๐ก๐ ๐ญ๐จ๐ฉ ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ซ๐ฌ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ ๐ฅ๐โฃโฃ
โฃโฃ
โ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ข ๐๐ณ๐ข๐ฏ๐ด๐ง๐ฐ๐ณ๐ฎ๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ธ๐ฉ๐บ ๐ธ๐ข๐ด ๐ช๐ต ๐ช๐ฏ๐ต๐ณ๐ฐ๐ฅ๐ถ๐ค๐ฆ๐ฅ?โฃโฃ
๐๐ต ๐ด๐ฐ๐ญ๐ท๐ฆ๐ฅ ๐ต๐ฉ๐ฆ ๐ญ๐ช๐ฎ๐ช๐ต๐ข๐ต๐ช๐ฐ๐ฏ๐ด ๐ฐ๐ง ๐๐๐๐ด & ๐๐๐๐๐ด ๐ฃ๐บ ๐ถ๐ด๐ช๐ฏ๐จ ๐ด๐ฆ๐ญ๐ง-๐ข๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ, ๐ฆ๐ฏ๐ข๐ฃ๐ญ๐ช๐ฏ๐จ ๐ฑ๐ข๐ณ๐ข๐ญ๐ญ๐ฆ๐ญ ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ด๐ช๐ฏ๐จ ๐ข๐ฏ๐ฅ ๐ค๐ข๐ฑ๐ต๐ถ๐ณ๐ช๐ฏ๐จ ๐ญ๐ฐ๐ฏ๐จ-๐ณ๐ข๐ฏ๐จ๐ฆ ๐ฅ๐ฆ๐ฑ๐ฆ๐ฏ๐ฅ๐ฆ๐ฏ๐ค๐ช๐ฆ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ฏ๐ฆ๐ท๐ฆ๐ณ ๐ฃ๐ฆ๐ง๐ฐ๐ณ๐ฆ!โฃโฃ
โฃโฃ
โ ๐๐ฆ๐ญ๐ง-๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ โ ๐๐ฉ๐ฆ ๐ฎ๐ข๐จ๐ช๐ค ๐ฃ๐ฆ๐ฉ๐ช๐ฏ๐ฅ ๐ช๐ตโฃโฃ
๐๐ท๐ฆ๐ณ๐บ ๐ธ๐ฐ๐ณ๐ฅ ๐ถ๐ฏ๐ฅ๐ฆ๐ณ๐ด๐ต๐ข๐ฏ๐ฅ๐ด ๐ช๐ต๐ด ๐ค๐ฐ๐ฏ๐ต๐ฆ๐น๐ต ๐ช๐ฏ ๐ณ๐ฆ๐ญ๐ข๐ต๐ช๐ฐ๐ฏ ๐ต๐ฐ ๐ฐ๐ต๐ฉ๐ฆ๐ณ๐ดโ๐ฎ๐ข๐ฌ๐ช๐ฏ๐จ ๐ฆ๐ฎ๐ฃ๐ฆ๐ฅ๐ฅ๐ช๐ฏ๐จ๐ด ๐ด๐ฎ๐ข๐ณ๐ต๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ๐ด ๐ฎ๐ฐ๐ณ๐ฆ ๐ค๐ฐ๐ฏ๐ต๐ฆ๐น๐ต-๐ข๐ธ๐ข๐ณ๐ฆ.โฃโฃ
โฃโฃ
โ ๐๐ถ๐ญ๐ต๐ช-๐๐ฆ๐ข๐ฅ ๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ โ ๐๐ฆ๐ฆ๐ช๐ฏ๐จ ๐ง๐ณ๐ฐ๐ฎ ๐ฎ๐ถ๐ญ๐ต๐ช๐ฑ๐ญ๐ฆ ๐ข๐ฏ๐จ๐ญ๐ฆ๐ดโฃโฃ
๐๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ต ๐ข๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ ๐ฉ๐ฆ๐ข๐ฅ๐ด ๐ง๐ฐ๐ค๐ถ๐ด ๐ฐ๐ฏ ๐ฅ๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ต ๐ณ๐ฆ๐ญ๐ข๐ต๐ช๐ฐ๐ฏ๐ด๐ฉ๐ช๐ฑ๐ด ๐ช๐ฏ ๐ต๐ฉ๐ฆ ๐ฅ๐ข๐ต๐ข. ๐๐ตโ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ฉ๐ข๐ท๐ช๐ฏ๐จ ๐ฎ๐ถ๐ญ๐ต๐ช๐ฑ๐ญ๐ฆ ๐ฆ๐น๐ฑ๐ฆ๐ณ๐ต๐ด ๐ข๐ฏ๐ข๐ญ๐บ๐ป๐ฆ ๐ต๐ฉ๐ฆ ๐ด๐ข๐ฎ๐ฆ ๐ช๐ฏ๐ง๐ฐ๐ณ๐ฎ๐ข๐ต๐ช๐ฐ๐ฏ!โฃโฃ
โฃโฃ
โ ๐๐ฐ๐ด๐ช๐ต๐ช๐ฐ๐ฏ๐ข๐ญ ๐๐ฏ๐ค๐ฐ๐ฅ๐ช๐ฏ๐จ โ ๐๐ฆ๐ข๐ค๐ฉ๐ช๐ฏ๐จ ๐ต๐ฉ๐ฆ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ ๐ฐ๐ณ๐ฅ๐ฆ๐ณ ๐ฎ๐ข๐ต๐ต๐ฆ๐ณ๐ดโฃโฃ
๐๐ช๐ฏ๐ค๐ฆ ๐๐ณ๐ข๐ฏ๐ด๐ง๐ฐ๐ณ๐ฎ๐ฆ๐ณ๐ด ๐ฅ๐ฐ๐ฏโ๐ต ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ด ๐ฅ๐ข๐ต๐ข ๐ด๐ฆ๐ฒ๐ถ๐ฆ๐ฏ๐ต๐ช๐ข๐ญ๐ญ๐บ, ๐ต๐ฉ๐ช๐ด ๐ต๐ณ๐ช๐ค๐ฌ ๐ฆ๐ฏ๐ด๐ถ๐ณ๐ฆ๐ด ๐ต๐ฉ๐ฆ๐บ โ๐ฌ๐ฏ๐ฐ๐ธโ ๐ต๐ฉ๐ฆ ๐ฑ๐ฐ๐ด๐ช๐ต๐ช๐ฐ๐ฏ ๐ฐ๐ง ๐ฆ๐ข๐ค๐ฉ ๐ต๐ฐ๐ฌ๐ฆ๐ฏ.โฃโฃ
โฃโฃ
โ ๐๐ข๐บ๐ฆ๐ณ ๐๐ฐ๐ณ๐ฎ๐ข๐ญ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏ โ ๐๐ต๐ข๐ฃ๐ช๐ญ๐ช๐ป๐ช๐ฏ๐จ ๐ต๐ฉ๐ฆ ๐ญ๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ดโฃโฃ
๐๐ต ๐ด๐ฑ๐ฆ๐ฆ๐ฅ๐ด ๐ถ๐ฑ ๐ต๐ณ๐ข๐ช๐ฏ๐ช๐ฏ๐จ ๐ข๐ฏ๐ฅ ๐ข๐ท๐ฐ๐ช๐ฅ๐ด ๐ท๐ข๐ฏ๐ช๐ด๐ฉ๐ช๐ฏ๐จ ๐จ๐ณ๐ข๐ฅ๐ช๐ฆ๐ฏ๐ต๐ด, ๐ญ๐ฆ๐ต๐ต๐ช๐ฏ๐จ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ๐ด ๐จ๐ฐ ๐ฅ๐ฆ๐ฆ๐ฑ๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ญ๐ฆ๐ข๐ณ๐ฏ ๐ฃ๐ฆ๐ต๐ต๐ฆ๐ณ.โฃโฃ
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โฃโฃ
โ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ข ๐๐ณ๐ข๐ฏ๐ด๐ง๐ฐ๐ณ๐ฎ๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ธ๐ฉ๐บ ๐ธ๐ข๐ด ๐ช๐ต ๐ช๐ฏ๐ต๐ณ๐ฐ๐ฅ๐ถ๐ค๐ฆ๐ฅ?โฃโฃ
๐๐ต ๐ด๐ฐ๐ญ๐ท๐ฆ๐ฅ ๐ต๐ฉ๐ฆ ๐ญ๐ช๐ฎ๐ช๐ต๐ข๐ต๐ช๐ฐ๐ฏ๐ด ๐ฐ๐ง ๐๐๐๐ด & ๐๐๐๐๐ด ๐ฃ๐บ ๐ถ๐ด๐ช๐ฏ๐จ ๐ด๐ฆ๐ญ๐ง-๐ข๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ, ๐ฆ๐ฏ๐ข๐ฃ๐ญ๐ช๐ฏ๐จ ๐ฑ๐ข๐ณ๐ข๐ญ๐ญ๐ฆ๐ญ ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ด๐ช๐ฏ๐จ ๐ข๐ฏ๐ฅ ๐ค๐ข๐ฑ๐ต๐ถ๐ณ๐ช๐ฏ๐จ ๐ญ๐ฐ๐ฏ๐จ-๐ณ๐ข๐ฏ๐จ๐ฆ ๐ฅ๐ฆ๐ฑ๐ฆ๐ฏ๐ฅ๐ฆ๐ฏ๐ค๐ช๐ฆ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ฏ๐ฆ๐ท๐ฆ๐ณ ๐ฃ๐ฆ๐ง๐ฐ๐ณ๐ฆ!โฃโฃ
โฃโฃ
โ ๐๐ฆ๐ญ๐ง-๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ โ ๐๐ฉ๐ฆ ๐ฎ๐ข๐จ๐ช๐ค ๐ฃ๐ฆ๐ฉ๐ช๐ฏ๐ฅ ๐ช๐ตโฃโฃ
๐๐ท๐ฆ๐ณ๐บ ๐ธ๐ฐ๐ณ๐ฅ ๐ถ๐ฏ๐ฅ๐ฆ๐ณ๐ด๐ต๐ข๐ฏ๐ฅ๐ด ๐ช๐ต๐ด ๐ค๐ฐ๐ฏ๐ต๐ฆ๐น๐ต ๐ช๐ฏ ๐ณ๐ฆ๐ญ๐ข๐ต๐ช๐ฐ๐ฏ ๐ต๐ฐ ๐ฐ๐ต๐ฉ๐ฆ๐ณ๐ดโ๐ฎ๐ข๐ฌ๐ช๐ฏ๐จ ๐ฆ๐ฎ๐ฃ๐ฆ๐ฅ๐ฅ๐ช๐ฏ๐จ๐ด ๐ด๐ฎ๐ข๐ณ๐ต๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ๐ด ๐ฎ๐ฐ๐ณ๐ฆ ๐ค๐ฐ๐ฏ๐ต๐ฆ๐น๐ต-๐ข๐ธ๐ข๐ณ๐ฆ.โฃโฃ
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โ ๐๐ถ๐ญ๐ต๐ช-๐๐ฆ๐ข๐ฅ ๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ โ ๐๐ฆ๐ฆ๐ช๐ฏ๐จ ๐ง๐ณ๐ฐ๐ฎ ๐ฎ๐ถ๐ญ๐ต๐ช๐ฑ๐ญ๐ฆ ๐ข๐ฏ๐จ๐ญ๐ฆ๐ดโฃโฃ
๐๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ต ๐ข๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ ๐ฉ๐ฆ๐ข๐ฅ๐ด ๐ง๐ฐ๐ค๐ถ๐ด ๐ฐ๐ฏ ๐ฅ๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ต ๐ณ๐ฆ๐ญ๐ข๐ต๐ช๐ฐ๐ฏ๐ด๐ฉ๐ช๐ฑ๐ด ๐ช๐ฏ ๐ต๐ฉ๐ฆ ๐ฅ๐ข๐ต๐ข. ๐๐ตโ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ฉ๐ข๐ท๐ช๐ฏ๐จ ๐ฎ๐ถ๐ญ๐ต๐ช๐ฑ๐ญ๐ฆ ๐ฆ๐น๐ฑ๐ฆ๐ณ๐ต๐ด ๐ข๐ฏ๐ข๐ญ๐บ๐ป๐ฆ ๐ต๐ฉ๐ฆ ๐ด๐ข๐ฎ๐ฆ ๐ช๐ฏ๐ง๐ฐ๐ณ๐ฎ๐ข๐ต๐ช๐ฐ๐ฏ!โฃโฃ
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โ ๐๐ฐ๐ด๐ช๐ต๐ช๐ฐ๐ฏ๐ข๐ญ ๐๐ฏ๐ค๐ฐ๐ฅ๐ช๐ฏ๐จ โ ๐๐ฆ๐ข๐ค๐ฉ๐ช๐ฏ๐จ ๐ต๐ฉ๐ฆ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ ๐ฐ๐ณ๐ฅ๐ฆ๐ณ ๐ฎ๐ข๐ต๐ต๐ฆ๐ณ๐ดโฃโฃ
๐๐ช๐ฏ๐ค๐ฆ ๐๐ณ๐ข๐ฏ๐ด๐ง๐ฐ๐ณ๐ฎ๐ฆ๐ณ๐ด ๐ฅ๐ฐ๐ฏโ๐ต ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ด ๐ฅ๐ข๐ต๐ข ๐ด๐ฆ๐ฒ๐ถ๐ฆ๐ฏ๐ต๐ช๐ข๐ญ๐ญ๐บ, ๐ต๐ฉ๐ช๐ด ๐ต๐ณ๐ช๐ค๐ฌ ๐ฆ๐ฏ๐ด๐ถ๐ณ๐ฆ๐ด ๐ต๐ฉ๐ฆ๐บ โ๐ฌ๐ฏ๐ฐ๐ธโ ๐ต๐ฉ๐ฆ ๐ฑ๐ฐ๐ด๐ช๐ต๐ช๐ฐ๐ฏ ๐ฐ๐ง ๐ฆ๐ข๐ค๐ฉ ๐ต๐ฐ๐ฌ๐ฆ๐ฏ.โฃโฃ
โฃโฃ
โ ๐๐ข๐บ๐ฆ๐ณ ๐๐ฐ๐ณ๐ฎ๐ข๐ญ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏ โ ๐๐ต๐ข๐ฃ๐ช๐ญ๐ช๐ป๐ช๐ฏ๐จ ๐ต๐ฉ๐ฆ ๐ญ๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ดโฃโฃ
๐๐ต ๐ด๐ฑ๐ฆ๐ฆ๐ฅ๐ด ๐ถ๐ฑ ๐ต๐ณ๐ข๐ช๐ฏ๐ช๐ฏ๐จ ๐ข๐ฏ๐ฅ ๐ข๐ท๐ฐ๐ช๐ฅ๐ด ๐ท๐ข๐ฏ๐ช๐ด๐ฉ๐ช๐ฏ๐จ ๐จ๐ณ๐ข๐ฅ๐ช๐ฆ๐ฏ๐ต๐ด, ๐ญ๐ฆ๐ต๐ต๐ช๐ฏ๐จ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ๐ด ๐จ๐ฐ ๐ฅ๐ฆ๐ฆ๐ฑ๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ญ๐ฆ๐ข๐ณ๐ฏ ๐ฃ๐ฆ๐ต๐ต๐ฆ๐ณ.โฃโฃ
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