What the Most Detailed Peer-Reviewed Study on AI in the Classroom Taught Us
and superb capabilities of widely available LLMs has ignited intense debate within the educational sector. On one side they offer ...
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The Automation Trap: Why Low-Code AI Models Fail When You Scale
In the , building Machine Learning models was a skill only data scientists with knowledge of Python could master. However, ...
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Boost 2-Bit LLM Accuracy with EoRA
is one of the key techniques for reducing the memory footprint of large language models (LLMs). It works by converting ...
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Survival Analysis When No One Dies: A Value-Based Approach
is a statistical approach used to answer the question: “How long will something last?” That “something” could range from a ...
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Load-Testing LLMs Using LLMPerf | Towards Data Science
Language Model (LLM) is not necessarily the final step in productionizing your Generative AI application. An often forgotten, yet crucial ...
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When Predictors Collide: Mastering VIF in Multicollinear Regression
In models, the independent variables must be not or only slightly dependent on each other, i.e. that they are not ...
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Attractors in Neural Network Circuits: Beauty and Chaos
The state space of the first two neuron activations over time follows an attractor. is one thing in common between ...
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Build Your Own AI Coding Assistant in JupyterLab with Ollama and Hugging Face
Jupyter AI brings generative AI capabilities right into the interface. Having a local AI assistant ensures privacy, reduces latency, and ...
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R.E.D.: Scaling Text Classification with Expert Delegation
With the new age of problem-solving augmented by Large Language Models (LLMs), only a handful of problems remain that have ...
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Evolving Product Operating Models in the Age of AI
previous article on organizing for AI (link), we looked at how the interplay between three key dimensions — ownership of ...
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