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Product-Oriented ML: A Guide for Data Scientists | by Jake Minns | Oct, 2024

How to build ML products users love. 23 min read · Oct 14, 2024 Photo by Pavel Danilyuk: https://www.pexels.com/photo/a-robot-holding-a-flower-8438979/Data science offers rich opportunities to explore new concepts and demonstrate their viability, all towards building the ‘intelligence’ behind features and products. However, most machine learning (ML) projects fail! And this isn’t just…

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Reinforcement Learning for Physics: ODEs and Hyperparameter Tuning | by Robert Etter | Oct, 2024

Working with ODEs Physical systems can typically be modeled through differential equations, or equations including derivatives. Forces, hence Newton’s Laws, can be expressed as derivatives, as can Maxwell’s Equations, so differential equations can describe most physics problems. A differential equation describes how a system changes based on the system’s current state, in effect defining state…

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LLM vs LLM: Codenames Tournament. A mini multi-agent competition among 3… | by Yennie Jun | Oct, 2024

A mini multi-agent competition among 3 different LLM agents Generated using ChatGPT 4o.This article was originally posted on Art Fish Intelligence. LLMs are good at many things, and one of those things is playing games. People have used LLMs to play all sorts of games such as Minecraft, Chess, murder mystery games, Werewolf, and the…

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Supercharge Your LLM Apps Using DSPy and Langfuse | by Raghav Bali | Oct, 2024

As illustrated in figure 1, DSPy is a pytorch-like/lego-like framework for building LLM-based apps. Out of the box, it comes with: Signatures: These are specifications to define input and output behaviour of a DSPy program. These can be defined using short-hand notation (like “question -> answer” where the framework automatically understands question is the input…

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