Matias Roldan
@roldan_matiasMobile apps developer. Passionate about AI and math♟🚴♂️
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[Repost] Why geometric computing for AI? IMHO, geometric calculus & computing is the Atlas of AI. Examples: tensor calculus, differential forms, symplectic calculus, cohomology, etc.
"While good mathematicians can answer interesting questions, great mathematicians can ask interesting questions." Jay Cummings
Starship rocket booster caught by tower
Why geometric computing for AI? IMHO, geometric calculus & computing is the Atlas of AI (eg., tensor calculus, symplectic calculus) x.com/SonyCSL/status…
“El álgebra no es más que geometría y la geometría no es más que álgebra abstracta” Marie-Sophie Germain(1776-1831)
How sophisticated is the maths in AI? Go on, have a guess. Because guessing is what AI itself would do. Terry Tao introduces his @OxUniMaths London Lecture, a journey through the potential for AI in science & maths. Watch from 5pm BST, Wed 7 August: youtu.be/_sTDSO74D8Q
En el Día Internacional de las Mujeres Matemáticas, Matilde Martínez, de @Udelaruy, y ℂ𝕒𝕞𝕚𝕟𝕠 𝕙𝕒𝕔𝕚𝕒 𝕟𝕚𝕟𝕘ú𝕟 𝕝𝕦𝕘𝕒𝕣, el nuevo episodio de #ConversacionesMatematicas por #MatematicaSentimental.
Mathematics and mystery. Shiver in ecstasy. The circle that fits precisely inside a 3-4-5 Pythagorean triangle has an area of π. OMG.
Revolucionando la #agricultura: Drones pulverizando para una gestión precisa y eficiente de los #cultivos.🌿🚁 Revolutionizing agriculture: Drones spraying for precise and efficient #crop management. 🌿🚁 #agtech #drones #precisionfarming
Prosperity is ahead for Argentina
“Sometimes science is more art than science, Morty”
Embracing the future of agriculture with AI! 🚜 From precision farming and crop monitoring 🌱 to predictive analytics 📊 and smart machinery 🤖, AI is revolutionizing how we grow food. Increased yields, reduced waste, and sustainable practices are within reach! 🌍🌱👩🌾 #agtech
Here's a short dynamics-based explanation of the equation e^(πi)=-1
Google launched 10 FREE courses on Generative AI 🔥 Each course can be completed in 1 day! Here is an overview and links for each of them:
I recommend the paper: Hidden Technical Debt in Machine Learning Systems, by D. Sculley to have a summary of the necessary components for the implementation of ML solutions in production proceedings.neurips.cc/paper_files/pa…
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