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Want to know the most common Machine Learning algorithms every AI student should learn? 🤖📘 Read our latest blog admecindia.co.in/machine-lea… #MachineLearning #AI #ArtificialIntelligence #MLAlgorithms #DataScience #LearnAI #TechStudents
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A complete Machine Learning Algorithms Cheatsheet in one image! From Linear Regression to Transformers — key formulas, pros/cons, best use cases & real-world applications Perfect for revision interviews. #MachineLearning #AI #DataScience #MLAlgorithms #DeepLearning #AICommunity
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Astrikos AI Gorilla Technology Groupからの戦略的投資を確保し、グローバルな成長 ow.ly/sL4850XRTJP #MachineLearning #AdversarialAI #SmartAI #AI #EnterpriseAI #GenerativeAI #GenAI #MLAlgorithms #DeepLearning #AIResearch #AIRevolution を加速させる #GRRR $GRRR
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SVM Explained with Examples Support Vector Machines find the best hyperplane to separate classes and maximize margin between closest points (support vectors). Great for classification! #AI #MachineLearning #MLAlgorithms How it works: Linear SVM for separable data; kernel trick (e.g., RBF) for non-linear. Equation: w·x b = 0. In scikit-learn: from sklearn.svm import SVC; svc.fit(X, y). #AITools #DataScience Examples: Iris flower classification (linear); XOR problem (kernel SVM). Handles high dimensions well, but scales poorly with large data. use for small/medium sets. #UnsupervisedML Pros: Effective in high-D space, memory efficient. Cons: Sensitive to noise, param tuning (C, gamma). Tune with GridSearchCV. #MLTips MNIST digits or spam detection. SVM success story? Reply examples or questions. let's demystify! #ArtificialIntelligence
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Are you ready for #NEURIPS 2025? 🔥🔥🔥Very Glad to join another year the #Conference on #Neural Information Processing Systems one of the 5 main 🌏events in the field @NeurIPSConf neurips.cc and one of the longest running since 1987 Do not miss the great keynote Invited talks by: 🚀 Richard Sutton "The Oak Architecture: A Vision of SuperIntelligence from Experience" 🚀 Zeynep Tufekci "Are We Having the Wrong Nightmares About AI?" 🚀 Yejin Choi "The Art of (Artificial) Reasoning" 🚀 Melanie Mitchell "On the Science of “Alien Intelligences”: Evaluating Cognitive Capabilities in Babies, Animals, and AI" 🚀 KyungHyun Cho "From Benchmarks to Problems - A Perspective on Problem Finding in AI" 🚀 Andrew Saxe "Demystifying depth: Principles of learning in deep neural networks" And the 100s of 🎤 Orals, 📜 Posters & 5858 Papers presented... Also with 14 promissing 🎓 Tutorials & 55 🛠️ Workshops, plenty of Expo, Socials & much more This years with venues in #SanDiego & #MexicoCity & #Eurips in #Copenhagen Thanks to the organizers & sponsors for making it possible one more year. Sad to see you all virtually this time, since I have several conflicting lectures to provide physically this week. Also our just released in preprint last paper by @josesanchezhb & myself #Neuraxon for @_Qubic_ Sience couldn't make it this year researchgate.net/publication… but it is already showing very promissing results that we will share soon in followup works, stay tunned. Meanwhile get the code and demo at github.com/DavidVivancos/Neu… & huggingface.co/spaces/DavidV… #AIResearch #MachineLearning #DeepLearning #GenerativeAI #NeuralNetworks #AIBreakthroughs #FutureOfAI #MLAlgorithms #AIEthics #DataScience #ReinforcementLearning #AIInnovation #LargeLanguageModels #AIMLCommunity #ComputationalNeuroscience #AIConference #ResearchHighlights #TechForGood #Artificiology
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🧠 Going back to the fundamentals of deep learning — Backpropagation, the algorithm that teaches neural networks how to learn from their mistakes. At its core, backpropagation is how models like GPT, Claude, and Gemini actually learn. It systematically adjusts internal parameters, weights and biases, to minimize prediction errors. Check out the thread for the full breakdown 👇 📅 Want to join live? Register now for the upcoming Agentic AI Bootcamp happening on Nov 25th. Don’t miss your chance to build, test, and evaluate intelligent agents! hubs.la/Q03Rz5tc0 #DeepLearning #Backpropagation #NeuralNetworks #MachineLearning #AI #DeepLearningBasics #GradientDescent #ArtificialIntelligence #MLAlgorithms #DataScience
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That’s the foundation! Next in the series, we’ll dive into specific algorithms like Linear Regression, K-Means, and beyond. Follow to learn... #MachineLearning #AI #MLAlgorithms #DeepLearning
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🤖 Machine Learning Algorithms – Quick Guide 🧭 Supervised Learning 📉 Regression – Linear, Polynomial 🌳 Decision Tree 🌲 Random Forest 📊 Classification – KNN, Logistic Regression, Naive Bayes, SVM 🔍 Unsupervised Learning 📦 Clustering – SVD, PCA, K-means 🔗 Association Analysis – Apriori, FP-Growth 🕵️ Hidden Markov Model 🎯 Reinforcement Learning – Agents learn by rewards & penalties 📌 Data Types 🔄 Continuous 🔢 Categorical #MachineLearning #AI #DataScience #MLAlgorithms #DeepLearning
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