M Rey

#Viral | 📹Se topó de frente con un jaguar a pocos metros de distancia. Vio transcurrir su vida en segundos, pero afortunadamente el enorme ejemplar se dio la media vuelta y se fue 😱😱😰😰🐆🐆 Ocurrió en lo espeso de la selva de Naachtún-Dos Lagunas, una reserva natural y sitio arqueológico ubicado en el municipio de Flores, Petén, en Guatemala, en el corazón de la Reserva de la Biosfera Maya, cerca de los límites con Calakmul. Francisco Asturias, vivió para contarlo y más, para compartir esta experiencia única.

PROBABILITY for DATA SCIENCE drive.google.com/file/d/1EGUjJA…

un tigre asustó a su madre por la espalda

Pocas veces la naturaleza te regala algo así. Una madre lince mudando a sus crías a un nuevo refugio. La emoción del momento fue indescriptible y no he podido guardármelo solo para mí. Dentro vídeo. 👇✨ Extremadura, naturalmente.

Machine Learning tips & tricks cheatsheet by Stanford University stanford.edu/~shervine/teac…

How to do projects while looking for a job

🦸Un hombre decidió arriesgar su vida al entrar a un túnel vehicular en Av. Ignacio L Vallarta, Zapopan, para poner a salvo a un 🐈 gatito atrapado y visiblemente asustado. El momento quedó grabado en video y rápidamente se volvió viral. 💬 ¿Qué opinas? 📹 Video: FB/ JJ Figueroa

Pro Machine Learning Algorithms drive.google.com/file/d/1ccOqw4…

O an… Trafik kazasında eşini kaybeden tavşan, başında ağlayarak gözyaşı döktü. (Filipinler)

🐻🌿 Captan a unos oseznos explorando y aprendiendo a sobrevivir junto a su madre tras nacer el pasado invierno.🥹

🐆💚 CAPTAN A MAMÁ JAGUAR Y SUS CRÍAS EN MÉXICO 😱🌿 😍 Un avistamiento considerado histórico mostró a una mamá jaguar junto a sus dos crías en la Sierra Gorda, llenando de esperanza a defensores de la biodiversidad en #Guanajuato; el hallazgo refleja la importancia de conservar los ecosistemas y proteger la fauna silvestre.

This 185-page book unlocks the secrets of deep learning. drive.google.com/file/d/188pV6F… Foundations > machine learning basics > computation efficiency > training methodologies Deep Models > activation functions > pooling > dropout > normalization > attention Architectures > MLPs > CNNs > attention Applications > image classification > object detection > speech recognition > reinforcement learning The Compute Schism > prompt engineering > quantization > adapters > model merging

Finalmente me salió la lámina del mundial que tanto quería ❤

"Introduction to Algorithms" is an extraordinary university-level resource for anyone studying algorithms and computer science. It covers computational complexity, data structures, graph algorithms, dynamic programming, divide and conquer methods, greedy algorithms, randomized algorithms, and many of the mathematical foundations underlying modern computer science. What makes it particularly valuable is the balance between mathematical rigor and practical algorithmic reasoning. It is one of those books that profoundly shapes the way you think about problems, efficiency, and computation itself. An absolute must-have in the toolkit of anyone working in computer science. cs.mcgill.ca/~akroit/math/c…

This guy literally dropped how to grow your brain like a muscle (in 4 mins)

Tekrarlama beyni yeniden yapılandırır. Tekrarlama beyni yeniden yapılandırır. Tekrarlama beyni yeniden yapılandırır. Tekrarlama beyni yeniden yapılandırır. Tekrarlama beyni yeniden yapılandırır. Tekrarlama beyni yeniden yapılandırır. Öğrenememek yoktur, tekrarlamamak vardır.

LEARN MACHINE LEARNING IN 100 DAYS FOR FREE This repo has 50.5k stars for a reason... it breaks down ml into a structured 100-day plan from data preprocessing & linear regression all the way to deep learning, clustering & neural networks Every concept comes with visual infographics & python code using scikit-learn no fluff, just a clear roadmap to go from zero to solid ml foundations github → github.com/Avik-Jain/100-…

🙀😹 Gatinha com deficiência física viraliza ao ser flagrada fugindo da tutora em alta velocidade 🤳 avanessabe | dommediabr

FREE Math Book. "A Course in Machine Learning" by Daumé. The study of algorithms that learn from data and experience, applied in a variety of applications, from medicine to advertising to militaries. Making sense of data. Topics: Decision Trees, Limits of Learning, Geometry and Nearest Neighbors, The Perceptron, Practical Issues, Beyond Binary Classification, Linear Models, Bias and Fairness, Probabilistic Modeling, Neural Networks, Kernel Methods, Learning Theory, Ensemble Methods, Efficient Learning, Unsupervised Learning, Expectation Maximization, Structured Prediction, Imitation Learning, etc. HTML Link: ciml.info PDF: ciml.info/dl/v0_9/ciml-v…