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@SeguInfoChannel · channel · Tech · indexed since 2026-07-19
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80posts in 30 days
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🎞Un poco largo, pero está bueno y se entiende la idea. No confíes y verifica!
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Hoy, conociendo a los frikis que nos acompañarán el viernes en Hacking Day (Paraná), le toca a @DBorgogno. Dan mostrará cómo explotar debilidades en la validación biométrica para evadir controles de prueba de vida y detección de deepfakes. Inscribite: https://www.hackingday.com.ar/
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MIT liberó su biblioteca de #IA & ML 100% FREE. ENJOY!😍 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 1. Foundations of Machine Learning https://cs.nyu.edu/~mohri/mlbook/ The mathematical backbone of ML - algorithms, theory, and how models actually learn. 2. Understanding Deep Learning https://udlbook.github.io/udlbook/ Neural networks explained visually and intuitively, from basics to modern architectures. 3. Deep Learning https://www.deeplearningbook.org/ The definitive deep learning reference, written by the researchers who shaped the field. 4. Introduction to Machine Learning Systems https://mlsysbook.ai/ How to design and build ML systems that work in production, not just in notebooks. 5. Algorithms for Optimization https://algorithmsbook.com/optimization/ The math behind how models improve - gradient methods, search, and decision-making. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 6. Reinforcement Learning: An Introduction http://incompleteideas.net/book/the-book.html The classic RL textbook - how agents learn to make decisions through trial and reward. 7. Distributional Reinforcement Learning https://www.distributional-rl.org/ Goes beyond average rewards to model the full distribution of outcomes. 8. Multi-Agent Reinforcement Learning https://www.marl-book.com/ How multiple AI agents learn, compete, and cooperate in shared environments. 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰 𝗠𝗟 9. Probabilistic Machine Learning: An Introduction https://probml.github.io/pml-book/book1.html ML through the lens of probability - uncertainty, inference, and Bayesian thinking. 10. Probabilistic Machine Learning: Advanced Topics https://probml.github.io/pml-book/book2.html Deep dives into probabilistic models, approximate inference, and generative methods. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 11. Agents in the Long Game of AI https://direct.mit.edu/books/oa-monograph/5779/Agents-in-the-Long-Game-of-AIComputational How to build AI agents that are trustworthy, hybrid, and designed for long-term reliability. 12. Fairness and Machine Learning https://fairmlboo
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Cristian (Segu-Info)
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Empezó la cuenta regresiva, nos vemos exactamente en 2 días en Paraná!
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