Forwarded from Machine Learning with Python
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📌 Causal Inference Is Eating Machine Learning
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-23 | ⏱️ Read time: 14 min read
Your ML model predicts perfectly but recommends wrong actions. Learn the 5-question diagnostic, method comparison…
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-23 | ⏱️ Read time: 14 min read
Your ML model predicts perfectly but recommends wrong actions. Learn the 5-question diagnostic, method comparison…
#DataScience #AI #Python
📌 Neuro-Symbolic Fraud Detection: Catching Concept Drift Before F1 Drops (Label-Free)
🗂 Category: DEEP LEARNING
🕒 Date: 2026-03-23 | ⏱️ Read time: 24 min read
This Article asks what happens next. The model has encoded its knowledge of fraud as…
#DataScience #AI #Python
🗂 Category: DEEP LEARNING
🕒 Date: 2026-03-23 | ⏱️ Read time: 24 min read
This Article asks what happens next. The model has encoded its knowledge of fraud as…
#DataScience #AI #Python
Forwarded from AI & ML Papers
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LLM Architecture Gallery — a page with cards for 39 models (2019–2026): DeepSeek, Qwen, Llama, Kimi, Grok, Nemotron, and others. For each — an architecture diagram, decoder type (dense / sparse MoE / hybrid), attention type, and links to technical reports and configs from HuggingFace.
It's clear how the market has converged on MoE + MLA for large models and why hybrid architectures (Mamba-2, DeltaNet, Lightning Attention) are gaining momentum.
https://sebastianraschka.com/llm-architecture-gallery/
https://t.me/DataScienceT
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It clearly presents all the main types of Neural Networks, with a brief theory and useful tips on Python for working with data and machine learning.
Essentially, it's a compilation of various cheat sheets in one convenient document.
https://www.bigdataheaven.com/wp-content/uploads/2019/02/AI-Neural-Networks.-22.pdf
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📌 How to Make Claude Code Improve from its Own Mistakes
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read
Supercharge Claude Code with continual learning
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read
Supercharge Claude Code with continual learning
#DataScience #AI #Python
Forwarded from Machine Learning with Python
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📌 From Dashboards to Decisions: Rethinking Data & Analytics in the Age of AI
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read
How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read
How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making
#DataScience #AI #Python
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📌 Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-24 | ⏱️ Read time: 18 min read
We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same…
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-24 | ⏱️ Read time: 18 min read
We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same…
#DataScience #AI #Python
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📌 The Complete Guide to AI Implementation for Chief Data & AI Officers in 2026
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-24 | ⏱️ Read time: 29 min read
How to leverage a framework to effectively prioritize AI Initiatives to rapidly accelerate growth and…
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-24 | ⏱️ Read time: 29 min read
How to leverage a framework to effectively prioritize AI Initiatives to rapidly accelerate growth and…
#DataScience #AI #Python
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📌 Following Up on Like-for-Like for Stores: Handling PY
🗂 Category: DATA ANALYSIS
🕒 Date: 2026-03-25 | ⏱️ Read time: 7 min read
My last article was about implementing Like-for-Like (L4L) for Stores. After discussing my solution with…
#DataScience #AI #Python
🗂 Category: DATA ANALYSIS
🕒 Date: 2026-03-25 | ⏱️ Read time: 7 min read
My last article was about implementing Like-for-Like (L4L) for Stores. After discussing my solution with…
#DataScience #AI #Python
📌 The Machine Learning Lessons I’ve Learned This Month
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-03-25 | ⏱️ Read time: 5 min read
Proactivity, blocking, and planning
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-03-25 | ⏱️ Read time: 5 min read
Proactivity, blocking, and planning
#DataScience #AI #Python
📌 Building Human-In-The-Loop Agentic Workflows
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-25 | ⏱️ Read time: 10 min read
Understanding how to set up human-in-the-loop (HITL) agentic workflows in LangGraph
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-25 | ⏱️ Read time: 10 min read
Understanding how to set up human-in-the-loop (HITL) agentic workflows in LangGraph
#DataScience #AI #Python
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📌 My Models Failed. That’s How I Became a Better Data Scientist.
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-25 | ⏱️ Read time: 9 min read
Data Leakage, Real-World Models, and the Path to Production AI in Healthcare
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-25 | ⏱️ Read time: 9 min read
Data Leakage, Real-World Models, and the Path to Production AI in Healthcare
#DataScience #AI #Python
📌 How to Make Your AI App Faster and More Interactive with Response Streaming
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-26 | ⏱️ Read time: 8 min read
In my latest posts, we’ve talked a lot about prompt caching as well as caching…
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-26 | ⏱️ Read time: 8 min read
In my latest posts, we’ve talked a lot about prompt caching as well as caching…
#DataScience #AI #Python
📌 Beyond Code Generation: AI for the Full Data Science Workflow
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-26 | ⏱️ Read time: 10 min read
Using Codex and MCP to connect Google Drive, GitHub, BigQuery, and analysis in one real workflow
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-26 | ⏱️ Read time: 10 min read
Using Codex and MCP to connect Google Drive, GitHub, BigQuery, and analysis in one real workflow
#DataScience #AI #Python
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📌 What the Bits-over-Random Metric Changed in How I Think About RAG and Agents
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-26 | ⏱️ Read time: 19 min read
Why retrieval that looks excellent on paper can still behave like noise in real RAG…
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-26 | ⏱️ Read time: 19 min read
Why retrieval that looks excellent on paper can still behave like noise in real RAG…
#DataScience #AI #Python
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