Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options
Human-guided AI collaboration
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A practical guide to observability, evaluations, and model comparisons
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Stop Blaming the Data: A Better Way to Handle Covariance Shift
Instead of using shift as an excuse for poor performance, use Inverse Probability Weighting to estimate how your model should perform in the new environment
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A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems
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Drift Detection in Robust Machine Learning Systems
A prerequisite for long-term success of machine learning systems
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EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas
How to build, score, and interpret RFM segments step by step
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Deep Reinforcement Learning: The Actor-Critic Method
Robot friends collaborate to learn to fly a drone
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Production-Ready LLMs Made Simple with the NeMo Agent Toolkit
From simple chat to multi-agent reasoning and real-time REST APIs
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The Machine Learning “Advent Calendar” Bonus 1: AUC in Excel
AUC measures how well a model ranks positives above negatives, independent of any chosen threshold.
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Implementing Vibe Proving with Reinforcement Learning
How to make LLMs reason with verifiable, step-by-step logic (Part 2)
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Exploring TabPFN: A Foundation Model Built for Tabular Data
Understanding the architecture, training pipeline and implementing TabPFN in practice
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How IntelliNode Automates Complex Workflows with Vibe Agents
Many AI systems focus on isolated tasks or simple prompt engineering. This approach allowed us to build interesting applications from a single prompt, but we are starting to hit a limit. Simple prompting falls short when we tackle complex AI tasks that require multiple stages or enterprise systems t...
Keeping Probabilities Honest: The Jacobian Adjustment
An intuitive explanation of transforming random variables correctly.
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The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel
An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings into contextual representations, illustrated with simple examples and an Excel-friendly walkthrough.
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Is Your Model Time-Blind? The Case for Cyclical Feature Encoding
How cyclical encoding improves machine learning prediction
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The Machine Learning “Advent Calendar” Day 23: CNN in Excel
A step-by-step 1D CNN for text, built in Excel, where every filter, weight, and decision is fully visible.
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Understanding the process behind agentic planning and task management in LangChain
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Stop Retraining Blindly: Use PSI to Build a Smarter Monitoring Pipeline
A data scientist's guide to population stability index (PSI)
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Synergy in Clicks: Harsanyi Dividends for E-Commerce
A brief overview of the math behind the Harsanyi Dividend and a real-world application in Streamlit
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The Machine Learning “Advent Calendar” Day 22: Embeddings in Excel
Understanding text embeddings through simple models and Excel
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ChatLLM Presents a Streamlined Solution to Addressing the Real Bottleneck in AI
For the last couple of years, a lot of the conversation around AI has revolved around a single, deceptively simple question: Which model is the best? But the next question was always, the best for what? The best for reasoning? Writing? Coding? Or maybe it’s the best for images, audio, or video? Tha...
What Happens When You Build an LLM Using Only 1s and 0s
An LLM that's 41× more efficient and 9× faster than today's standard models
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