Hamiltonian mechanics is a way to describe how physical systems, like planets or pendulums, move over time, focusing on energy rather than just forces. By reframing complex dynamics through energy lenses, this 19th-century physics framework now powers cutting-edge generative AI. It uses generalized coordinates ( q ) (like position) and their conjugate momenta ( p ) (related to momentum), forming a phase space that captures the system's state. This approach is particularly useful for complex systems with many parts, making it easier to find patterns and conservation laws.
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How to Format Your TDS Draft: A Quick(ish) Guide
Everything you need to know about creating a draft on our Contributor Portal
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Data Science: From School to Work, Part III
Good practices for Python error handling and logging
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The post Data Science: From School to Work, Part III appeared first on Towards Data Science.
Japanese-Chinese Translation with GenAI: What Works and What Doesn’t
Evaluating GenAI in Japanese-Chinese translation: current limits and opportunities
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The post Japanese-Chinese Translation with GenAI: What Works and What Doesn’t appeared first on Towards Data Science.
Talk to Videos
Developing an interactive AI application for video-based learning in education and business
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The post Talk to Videos appeared first on Towards Data Science.
Automate Supply Chain Analytics Workflows with AI Agents using n8n
What if you could automate complete supply chain analytics workflows with low-code solutions?
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The post Automate Supply Chain Analytics Workflows with AI Agents using n8n appeared first on Towards Data Science.
AI Agents from Zero to Hero — Part 2
Intro In Part 1 of this tutorial series, we introduced AI Agents, autonomous programs that perform tasks, make decisions, and communicate with others. Agents perform actions through Tools. It might happen that a Tool doesn’t work on the first try, or that multiple Tools must be activated in sequence. Agents should be able to organize […]
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Uncertainty Quantification in Machine Learning with an Easy Python Interface
The ML Uncertainty Package
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