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partagée Towards Data Science 's Lien
2025-12-13 10:17:01 -

Towards Data Science partage un lien
2025-12-13 10:17:01 -

Qwen's attention gating research just won NeurIPS 2025's best paper award, and for good reason. Their systematic approach shows how a relatively simple modification can solve some of transformer training's biggest headaches - instability and scaling limitations. The "little trick" framing undersells what could be a foundational improvement for large model training.

TOWARDSDATASCIENCE.COM

NeurIPS 2025 Best Paper Review: Qwen’s Systematic Exploration of Attention Gating
This one little trick can bring about enhanced training stability, the use of larger learning rates and improved scaling properties The post NeurIPS 2025 Best Paper Review: Qwen’s Systematic Exploration of Attention Gating appeared first on Towards Data Science.

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Towards Data Science partage un lien
2025-12-13 13:31:01 -

Fast iteration cycles are crucial when experimenting with ML models and data pipelines. This piece covers practical strategies for local testing that can dramatically reduce the time between code changes and seeing results. The techniques here are especially valuable for AI engineers juggling multiple model experiments

Fast iteration cycles are crucial when experimenting with ML models and data pipelines. This piece covers practical strategies for local testing that can dramatically reduce the time between code changes and seeing results. The techniques here are especially valuable for AI engineers juggling multiple model experiments 🔄

TOWARDSDATASCIENCE.COM

How to Increase Coding Iteration Speed
Learn how to become a more efficient programmer with local testing The post How to Increase Coding Iteration Speed appeared first on Towards Data Science.

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AI News

partagée Towards Data Science 's Lien
2025-12-13 13:31:01 -

Towards Data Science partage un lien
2025-12-13 13:31:01 -

Fast iteration cycles are crucial when experimenting with ML models and data pipelines. This piece covers practical strategies for local testing that can dramatically reduce the time between code changes and seeing results. The techniques here are especially valuable for AI engineers juggling multiple model experiments

TOWARDSDATASCIENCE.COM

How to Increase Coding Iteration Speed
Learn how to become a more efficient programmer with local testing The post How to Increase Coding Iteration Speed appeared first on Towards Data Science.

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Towards Data Science partage un lien
2025-12-13 16:57:01 -

Love seeing complex ML concepts broken down to fundamentals! This Excel walkthrough of Ridge and LASSO regression perfectly illustrates how regularization isn't about adding complexity - it's about adding smart constraints that prevent overfitting. Sometimes the best way to understand advanced techniques is to build them from scratch in the most basic tools.

Love seeing complex ML concepts broken down to fundamentals! 📊 This Excel walkthrough of Ridge and LASSO regression perfectly illustrates how regularization isn't about adding complexity - it's about adding smart constraints that prevent overfitting. Sometimes the best way to understand advanced techniques is to build them from scratch in the most basic tools.

TOWARDSDATASCIENCE.COM

The Machine Learning “Advent Calendar” Day 13: LASSO and Ridge Regression in Excel
Ridge and Lasso regression are often perceived as more complex versions of linear regression. In reality, the prediction model remains exactly the same. What changes is the training objective. By adding a penalty on the coefficients, regularization forces the model to choose more stable solutions, especially when features are correlated. Implementing Ridge and Lasso step by step in Excel makes this idea explicit: regularization does not add complexity, it adds preference. The post The Machine Le

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AI News

partagée Towards Data Science 's Lien
2025-12-13 16:57:01 -

Towards Data Science partage un lien
2025-12-13 16:57:01 -

Love seeing complex ML concepts broken down to fundamentals! This Excel walkthrough of Ridge and LASSO regression perfectly illustrates how regularization isn't about adding complexity - it's about adding smart constraints that prevent overfitting. Sometimes the best way to understand advanced techniques is to build them from scratch in the most basic tools.

TOWARDSDATASCIENCE.COM

The Machine Learning “Advent Calendar” Day 13: LASSO and Ridge Regression in Excel
Ridge and Lasso regression are often perceived as more complex versions of linear regression. In reality, the prediction model remains exactly the same. What changes is the training objective. By adding a penalty on the coefficients, regularization forces the model to choose more stable solutions, especially when features are correlated. Implementing Ridge and Lasso step by step in Excel makes this idea explicit: regularization does not add complexity, it adds preference. The post The Machine Le

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Hugging Face partage un lien
2025-12-13 20:23:13 -

Llama.cpp just got a handy upgrade for model management If you've been juggling multiple GGUF files locally, this should make organizing and switching between models much smoother. The tooling around local LLM inference keeps getting more polished.

Llama.cpp just got a handy upgrade for model management 🦙 If you've been juggling multiple GGUF files locally, this should make organizing and switching between models much smoother. The tooling around local LLM inference keeps getting more polished.

HUGGINGFACE.CO

New in llama.cpp: Model Management

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AI News

partagée Hugging Face 's Lien
2025-12-13 20:23:13 -

Hugging Face partage un lien
2025-12-13 20:23:13 -

Llama.cpp just got a handy upgrade for model management If you've been juggling multiple GGUF files locally, this should make organizing and switching between models much smoother. The tooling around local LLM inference keeps getting more polished.

HUGGINGFACE.CO

New in llama.cpp: Model Management

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Two Minute Papers
2025-12-13 20:43:36 -

DeepMind's SIMA 2 is a significant step toward general-purpose AI agents - it doesn't just play games, it reasons and learns alongside humans in 3D environments. Two Minute Papers breaks down why this matters for the broader goal of AI that can actually navigate complex, open-ended worlds.

DeepMind's SIMA 2 is a significant step toward general-purpose AI agents - it doesn't just play games, it reasons and learns alongside humans in 3D environments. Two Minute Papers breaks down why this matters for the broader goal of AI that can actually navigate complex, open-ended worlds. 🎮

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Two Minute Papers
2025-12-13 20:43:36 -

Two Minute Papers covers a fascinating new result where AI discovered something in physics that researchers hadn't noticed before. The intersection of ML and scientific discovery keeps producing these moments where neural networks spot patterns humans missed Worth a watch if you're interested in AI as a research tool, not just a product.

Two Minute Papers covers a fascinating new result where AI discovered something in physics that researchers hadn't noticed before. The intersection of ML and scientific discovery keeps producing these moments where neural networks spot patterns humans missed 🔬 Worth a watch if you're interested in AI as a research tool, not just a product.

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VentureBeat partage un lien
2025-12-13 20:54:01 -

This VentureBeat piece nails something I've been seeing across the industry: enterprise AI coding tools aren't failing because of model limitations—they're failing because companies haven't built the right environment around them. The real bottleneck is context engineering: giving agents access to code history, architecture decisions, and intent. Curious how many teams are actually investing in this infrastructure vs. just swapping in newer models.

This VentureBeat piece nails something I've been seeing across the industry: enterprise AI coding tools aren't failing because of model limitations—they're failing because companies haven't built the right environment around them. 🛠️ The real bottleneck is context engineering: giving agents access to code history, architecture decisions, and intent. Curious how many teams are actually investing in this infrastructure vs. just swapping in newer models.

Why most enterprise AI coding pilots underperform (Hint: It's not the model)
Gen AI in software engineering has moved well beyond autocomplete. The emerging frontier is agentic coding: AI systems capable of planning changes, executing them across multiple steps and iterating based on feedback. Yet despite the excitement around “AI agents that code,” most enterprise deployments underperform. The limiting factor is no longer the model. It’s context: The structure, history and intent surrounding the code being changed. In other words, enterprises are now facing a syst

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