📌 4 Techniques to Optimize AI Coding Efficiency
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-24 | ⏱️ Read time: 8 min read
Learn how to code more effectively using AI
#DataScience #AI #Python
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-24 | ⏱️ Read time: 8 min read
Learn how to code more effectively using AI
#DataScience #AI #Python
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📌 Bonferroni vs. Benjamini-Hochberg: Choosing Your P-Value Correction
🗂 Category: STATISTICS
🕒 Date: 2025-12-24 | ⏱️ Read time: 11 min read
Multiple hypothesis testing, P-values, and Monte Carlo
#DataScience #AI #Python
🗂 Category: STATISTICS
🕒 Date: 2025-12-24 | ⏱️ Read time: 11 min read
Multiple hypothesis testing, P-values, and Monte Carlo
#DataScience #AI #Python
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📌 Keeping Probabilities Honest: The Jacobian Adjustment
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-25 | ⏱️ Read time: 10 min read
An intuitive explanation of transforming random variables correctly.
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-25 | ⏱️ Read time: 10 min read
An intuitive explanation of transforming random variables correctly.
#DataScience #AI #Python
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📌 Why MAP and MRR Fail for Search Ranking (and What to Use Instead)
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-25 | ⏱️ Read time: 9 min read
MAP and MRR look intuitive, but they quietly break ranking evaluation. Here’s why these metrics…
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-25 | ⏱️ Read time: 9 min read
MAP and MRR look intuitive, but they quietly break ranking evaluation. Here’s why these metrics…
#DataScience #AI #Python
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Forwarded from ML Research Hub
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/
👉 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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📌 Think Your Python Code Is Slow? Stop Guessing and Start Measuring
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-26 | ⏱️ Read time: 13 min read
A hands-on tour of using cProfile + SnakeViz to find (and fix) the “hot” paths…
#DataScience #AI #Python
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-26 | ⏱️ Read time: 13 min read
A hands-on tour of using cProfile + SnakeViz to find (and fix) the “hot” paths…
#DataScience #AI #Python
❤3
📌 How to Build an AI-Powered Weather ETL Pipeline with Databricks and GPT-4o: From API To Dashboard
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-12-26 | ⏱️ Read time: 11 min read
A step-by-step guide from weather API ETL to dashboard on Databricks
#DataScience #AI #Python
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-12-26 | ⏱️ Read time: 11 min read
A step-by-step guide from weather API ETL to dashboard on Databricks
#DataScience #AI #Python
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Forwarded from Machine Learning with Python
transformer Q&A.pdf
1.3 MB
𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 🔥👇
✅ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘸𝘢𝘴 𝘪𝘵 𝘪𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦𝘥?
𝘐𝘵 𝘴𝘰𝘭𝘷𝘦𝘥 𝘵𝘩𝘦 𝘭𝘪𝘮𝘪𝘵𝘢𝘵𝘪𝘰𝘯𝘴 𝘰𝘧 𝘙𝘕𝘕𝘴 & 𝘓𝘚𝘛𝘔𝘴 𝘣𝘺 𝘶𝘴𝘪𝘯𝘨 𝘴𝘦𝘭𝘧-𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯, 𝘦𝘯𝘢𝘣𝘭𝘪𝘯𝘨 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 𝘢𝘯𝘥 𝘤𝘢𝘱𝘵𝘶𝘳𝘪𝘯𝘨 𝘭𝘰𝘯𝘨-𝘳𝘢𝘯𝘨𝘦 𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘤𝘪𝘦𝘴 𝘭𝘪𝘬𝘦 𝘯𝘦𝘷𝘦𝘳 𝘣𝘦𝘧𝘰𝘳𝘦!
✅ 𝘚𝘦𝘭𝘧-𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 – 𝘛𝘩𝘦 𝘮𝘢𝘨𝘪𝘤 𝘣𝘦𝘩𝘪𝘯𝘥 𝘪𝘵
𝘌𝘷𝘦𝘳𝘺 𝘸𝘰𝘳𝘥 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘴 𝘪𝘵𝘴 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘪𝘯 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯 𝘵𝘰 𝘰𝘵𝘩𝘦𝘳𝘴—𝘮𝘢𝘬𝘪𝘯𝘨 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴 𝘴𝘮𝘢𝘳𝘵𝘦𝘳 𝘢𝘯𝘥 𝘮𝘰𝘥𝘦𝘭𝘴 𝘮𝘰𝘳𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵-𝘢𝘸𝘢𝘳𝘦.
✅ 𝘔𝘶𝘭𝘵𝘪-𝘏𝘦𝘢𝘥 𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 – 𝘚𝘦𝘦𝘪𝘯𝘨 𝘧𝘳𝘰𝘮 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘢𝘯𝘨𝘭𝘦𝘴
𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘩𝘦𝘢𝘥𝘴 𝘧𝘰𝘤𝘶𝘴 𝘰𝘯 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢. 𝘐𝘵’𝘴 𝘭𝘪𝘬𝘦 𝘩𝘢𝘷𝘪𝘯𝘨 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘦𝘹𝘱𝘦𝘳𝘵𝘴 𝘢𝘯𝘢𝘭𝘺𝘻𝘦 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯!
✅ 𝘗𝘰𝘴𝘪𝘵𝘪𝘰𝘯𝘢𝘭 𝘌𝘯𝘤𝘰𝘥𝘪𝘯𝘨 – 𝘛𝘦𝘢𝘤𝘩𝘪𝘯𝘨 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘰𝘳𝘥𝘦𝘳 𝘮𝘢𝘵𝘵𝘦𝘳𝘴
𝘚𝘪𝘯𝘤𝘦 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳𝘴 𝘥𝘰𝘯’𝘵 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘥𝘢𝘵𝘢 𝘴𝘦𝘲𝘶𝘦𝘯𝘵𝘪𝘢𝘭𝘭𝘺, 𝘵𝘩𝘪𝘴 𝘵𝘳𝘪𝘤𝘬 𝘦𝘯𝘴𝘶𝘳𝘦𝘴 𝘵𝘩𝘦𝘺 “𝘬𝘯𝘰𝘸” 𝘵𝘩𝘦 𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯 𝘰𝘧 𝘦𝘢𝘤𝘩 𝘵𝘰𝘬𝘦𝘯.
✅ 𝘓𝘢𝘺𝘦𝘳 𝘕𝘰𝘳𝘮𝘢𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 – 𝘚𝘵𝘢𝘣𝘪𝘭𝘪𝘻𝘪𝘯𝘨 𝘵𝘩𝘦 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘱𝘳𝘰𝘤𝘦𝘴𝘴
𝘐𝘵 𝘴𝘱𝘦𝘦𝘥𝘴 𝘶𝘱 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘢𝘯𝘥 𝘢𝘷𝘰𝘪𝘥𝘴 𝘷𝘢𝘯𝘪𝘴𝘩𝘪𝘯𝘨 𝘨𝘳𝘢𝘥𝘪𝘦𝘯𝘵𝘴, 𝘭𝘦𝘵𝘵𝘪𝘯𝘨 𝘮𝘰𝘥𝘦𝘭𝘴 𝘨𝘰 𝘥𝘦𝘦𝘱𝘦𝘳 𝘢𝘯𝘥 𝘭𝘦𝘢𝘳𝘯 𝘣𝘦𝘵𝘵𝘦𝘳.
👉 @codeprogrammer
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✅ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘸𝘢𝘴 𝘪𝘵 𝘪𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦𝘥?
𝘐𝘵 𝘴𝘰𝘭𝘷𝘦𝘥 𝘵𝘩𝘦 𝘭𝘪𝘮𝘪𝘵𝘢𝘵𝘪𝘰𝘯𝘴 𝘰𝘧 𝘙𝘕𝘕𝘴 & 𝘓𝘚𝘛𝘔𝘴 𝘣𝘺 𝘶𝘴𝘪𝘯𝘨 𝘴𝘦𝘭𝘧-𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯, 𝘦𝘯𝘢𝘣𝘭𝘪𝘯𝘨 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 𝘢𝘯𝘥 𝘤𝘢𝘱𝘵𝘶𝘳𝘪𝘯𝘨 𝘭𝘰𝘯𝘨-𝘳𝘢𝘯𝘨𝘦 𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘤𝘪𝘦𝘴 𝘭𝘪𝘬𝘦 𝘯𝘦𝘷𝘦𝘳 𝘣𝘦𝘧𝘰𝘳𝘦!
✅ 𝘚𝘦𝘭𝘧-𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 – 𝘛𝘩𝘦 𝘮𝘢𝘨𝘪𝘤 𝘣𝘦𝘩𝘪𝘯𝘥 𝘪𝘵
𝘌𝘷𝘦𝘳𝘺 𝘸𝘰𝘳𝘥 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘴 𝘪𝘵𝘴 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘪𝘯 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯 𝘵𝘰 𝘰𝘵𝘩𝘦𝘳𝘴—𝘮𝘢𝘬𝘪𝘯𝘨 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴 𝘴𝘮𝘢𝘳𝘵𝘦𝘳 𝘢𝘯𝘥 𝘮𝘰𝘥𝘦𝘭𝘴 𝘮𝘰𝘳𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵-𝘢𝘸𝘢𝘳𝘦.
✅ 𝘔𝘶𝘭𝘵𝘪-𝘏𝘦𝘢𝘥 𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 – 𝘚𝘦𝘦𝘪𝘯𝘨 𝘧𝘳𝘰𝘮 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘢𝘯𝘨𝘭𝘦𝘴
𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘩𝘦𝘢𝘥𝘴 𝘧𝘰𝘤𝘶𝘴 𝘰𝘯 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢. 𝘐𝘵’𝘴 𝘭𝘪𝘬𝘦 𝘩𝘢𝘷𝘪𝘯𝘨 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘦𝘹𝘱𝘦𝘳𝘵𝘴 𝘢𝘯𝘢𝘭𝘺𝘻𝘦 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯!
✅ 𝘗𝘰𝘴𝘪𝘵𝘪𝘰𝘯𝘢𝘭 𝘌𝘯𝘤𝘰𝘥𝘪𝘯𝘨 – 𝘛𝘦𝘢𝘤𝘩𝘪𝘯𝘨 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘰𝘳𝘥𝘦𝘳 𝘮𝘢𝘵𝘵𝘦𝘳𝘴
𝘚𝘪𝘯𝘤𝘦 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳𝘴 𝘥𝘰𝘯’𝘵 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘥𝘢𝘵𝘢 𝘴𝘦𝘲𝘶𝘦𝘯𝘵𝘪𝘢𝘭𝘭𝘺, 𝘵𝘩𝘪𝘴 𝘵𝘳𝘪𝘤𝘬 𝘦𝘯𝘴𝘶𝘳𝘦𝘴 𝘵𝘩𝘦𝘺 “𝘬𝘯𝘰𝘸” 𝘵𝘩𝘦 𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯 𝘰𝘧 𝘦𝘢𝘤𝘩 𝘵𝘰𝘬𝘦𝘯.
✅ 𝘓𝘢𝘺𝘦𝘳 𝘕𝘰𝘳𝘮𝘢𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 – 𝘚𝘵𝘢𝘣𝘪𝘭𝘪𝘻𝘪𝘯𝘨 𝘵𝘩𝘦 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘱𝘳𝘰𝘤𝘦𝘴𝘴
𝘐𝘵 𝘴𝘱𝘦𝘦𝘥𝘴 𝘶𝘱 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘢𝘯𝘥 𝘢𝘷𝘰𝘪𝘥𝘴 𝘷𝘢𝘯𝘪𝘴𝘩𝘪𝘯𝘨 𝘨𝘳𝘢𝘥𝘪𝘦𝘯𝘵𝘴, 𝘭𝘦𝘵𝘵𝘪𝘯𝘨 𝘮𝘰𝘥𝘦𝘭𝘴 𝘨𝘰 𝘥𝘦𝘦𝘱𝘦𝘳 𝘢𝘯𝘥 𝘭𝘦𝘢𝘳𝘯 𝘣𝘦𝘵𝘵𝘦𝘳.
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📌 Exploring TabPFN: A Foundation Model Built for Tabular Data
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-27 | ⏱️ Read time: 11 min read
Understanding the architecture, training pipeline and implementing TabPFN in practice
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-27 | ⏱️ Read time: 11 min read
Understanding the architecture, training pipeline and implementing TabPFN in practice
#DataScience #AI #Python
❤1
📌 How IntelliNode Automates Complex Workflows with Vibe Agents
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-27 | ⏱️ Read time: 6 min read
Many AI systems focus on isolated tasks or simple prompt engineering. This approach allowed us…
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-27 | ⏱️ Read time: 6 min read
Many AI systems focus on isolated tasks or simple prompt engineering. This approach allowed us…
#DataScience #AI #Python
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📌 Hugging Face Transformers in Action: Learning How To Leverage AI for NLP
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-28 | ⏱️ Read time: 9 min read
A practical guide to Hugging Face Transformers and to how you can analyze your resumé…
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-28 | ⏱️ Read time: 9 min read
A practical guide to Hugging Face Transformers and to how you can analyze your resumé…
#DataScience #AI #Python
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📌 Breaking the Hardware Barrier: Software FP8 for Older GPUs
🗂 Category: DEEP LEARNING
🕒 Date: 2025-12-28 | ⏱️ Read time: 8 min read
Deep learning workloads are increasingly memory-bound, with GPU cores sitting idle while waiting for data…
#DataScience #AI #Python
🗂 Category: DEEP LEARNING
🕒 Date: 2025-12-28 | ⏱️ Read time: 8 min read
Deep learning workloads are increasingly memory-bound, with GPU cores sitting idle while waiting for data…
#DataScience #AI #Python
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🚀 Master Data Science & Programming!
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🖊 Data Science Jupyter Notebooks
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Admin: @HusseinSheikho
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
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.
https://t.me/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
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Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.me/Python53
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
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Admin: @HusseinSheikho
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📌 How to Facilitate Effective AI Programming
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-29 | ⏱️ Read time: 7 min read
How to ensure your coding agent has the same context as you
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-29 | ⏱️ Read time: 7 min read
How to ensure your coding agent has the same context as you
#DataScience #AI #Python
📌 Machine Learning vs AI Engineer: What Are the Differences?
🗂 Category: CAREER ADVICE
🕒 Date: 2025-12-29 | ⏱️ Read time: 7 min read
One of the most confusing questions in tech right now is: What is the difference…
#DataScience #AI #Python
🗂 Category: CAREER ADVICE
🕒 Date: 2025-12-29 | ⏱️ Read time: 7 min read
One of the most confusing questions in tech right now is: What is the difference…
#DataScience #AI #Python
❤1
📌 Implementing Vibe Proving with Reinforcement Learning
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-29 | ⏱️ Read time: 9 min read
How to make LLMs reason with verifiable, step-by-step logic (Part 2)
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-29 | ⏱️ Read time: 9 min read
How to make LLMs reason with verifiable, step-by-step logic (Part 2)
#DataScience #AI #Python
❤1
📌 Overcoming Nonsmoothness and Control Chattering in Nonconvex Optimal Control Problems
🗂 Category: ROBOTICS
🕒 Date: 2025-12-30 | ⏱️ Read time: 17 min read
With some hints for good numerics
#DataScience #AI #Python
🗂 Category: ROBOTICS
🕒 Date: 2025-12-30 | ⏱️ Read time: 17 min read
With some hints for good numerics
#DataScience #AI #Python
📌 The Machine Learning “Advent Calendar” Bonus 1: AUC in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-30 | ⏱️ Read time: 8 min read
AUC measures how well a model ranks positives above negatives, independent of any chosen threshold.
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-30 | ⏱️ Read time: 8 min read
AUC measures how well a model ranks positives above negatives, independent of any chosen threshold.
#DataScience #AI #Python
Forwarded from Machine Learning with Python
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📌 Agents Under the Curve (AUC)
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-30 | ⏱️ Read time: 15 min read
Towards understanding if your agentic solution is actually better
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-30 | ⏱️ Read time: 15 min read
Towards understanding if your agentic solution is actually better
#DataScience #AI #Python
❤2