Which algorithm from this list have YOU already tried? Drop it in the comments #MachineLearning #DataScience #MLAlgorithms #LearningML #ArtificialIntelligence #MLFromScratch #SupervisedLearning #UnsupervisedLearning #ReinforcementLearning #DataScientist #TechSimplified

🚀 K-Means Clustering Visualized Watch how random centroids evolve into meaningful clusters step-by-step A simple yet powerful algorithm behind modern AI & data science Learn • Visualize • Understand #KMeans #MachineLearning #DataScience #AI #Clustering #UnsupervisedLearning #Maths #Mathematics #DataVisualization #Algorithm #DeepLearning #Analytics #Tech #Coding #Python #AICommunity #LearnAI #STEM #BigData #ArtificialIntelligence #ML #Science #Engineering #DataAnalytics #Visualization #TechEducation

Excited to share that our paper has been accepted to the ACL 2026 (@aclmeeting) Main Conference. 🎉 Grateful to my co-authors and advisor, and looking forward to presenting it in San Diego this July. 🌴☀️🌮 #ACL2026 #ACL #LLM #NLP #AI #MachineLearning #UnsupervisedLearning

We often hear that Machine Learning models learn patterns in data. But what does that actually look like in Geometry? If you dropped a little elastic mesh into a cloud of points and let it learn, how would it fold itself to match the shape of the data? In this scene we watch a Self-Organizing Map (SOM), a simple unsupervised neural model, learn the shape of a 3D datasets l, one static and the other dynamic. On top of this, we lay down a square grid of neurons whose weights live in the same plane. At the start, this grid is just a flat net floating across the cloud. It knows nothing about the structure underneath. Learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, and then nudge that neuron and its neighbours toward the point. Do this again and again, while slowly shrinking how far the neighbourhood influence spreads. Python code is available for Subscribers. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML

DynMoCo: A Novel AI Framework to Reveal Modular Substructures of Protein From Molecular Dynamics 1 DynMoCo introduces the first end-to-end deep learning framework specifically designed for dynamic community detection on molecular graphs, addressing a critical gap in analyzing molecular dynamics simulations. 2 The framework combines graph convolutional networks with recurrent neural networks to track how protein substructures evolve over time, transforming high-dimensional MD data into interpretable communities. 3 Unlike static methods that suffer from label switching and temporal inconsistency, DynMoCo learns temporally consistent representations that maintain community identity across frames, enabling tracking of community emergence, splitting, and merging events. 4 The model incorporates knowledge-informed modularity loss that leverages known protein domain structures to guide community detection, ensuring biologically meaningful results without requiring labeled training data. 5 Applied to three integrin systems under force-ramp and force-clamp simulations, DynMoCo identified ~30 communities per system and revealed that αVβ3 exhibits cross-domain coupling while αIIbβ3 shows modular domain architecture despite sharing identical β3 chains. 6 The analysis uncovered mechanically responsive residues at community interfaces, including Thr157 in α5β1 which explains experimental observations about integrin activation through hydrogen bond network disruption. 7 DynMoCo outperformed existing benchmarks including DMoN, DEC, DynAERNN, and MFC-TopoReg in both modularity and conductance metrics across all tested datasets. 8 The authors provide open-source code and VMD visualization scripts to enable interactive exploration of dynamic communities on 3D protein structures. 💻Code: github.com/lingchm/DynMoCo 📜Paper: biorxiv.org/content/10.64898… #MolecularDynamics #ProteinDynamics #DeepLearning #GraphNeuralNetworks #Integrin #StructuralBiology #Bioinformatics #UnsupervisedLearning

Day 44/100 - K-clusters with visualization. Went for volleyball training, just did the little I could do. We go again tomorrow! #unsupervisedlearning #kmeans #kclusters

Day 43/ 100 - Unsupervised learning Unsupervised learning is basically a machine learning without labels. It’s about finding structure or patterns in data when no one tells the algorithm what’s “right” or “wrong.” #unsupervisedlearning #machinelearning

🤖 Supervised vs. Unsupervised Learning: The Quick Guide. Hello everyone! So, In the world of AI, the difference comes down to one thing: Guidance! 1. Supervised Learning (The "Teacher" Model) Think of this as a student with an answer key. You feed the model labeled data (input correct output). The goal is for the AI to learn the relationship between the two so it can predict outcomes for new data. *Common tasks: Spam detection, price forecasting, image recognition. •*Analogy: Teaching a kid what an apple is by showing them 100 pictures and saying, "This is an apple." 2. Unsupervised Learning (The "Explorer" Model) Here, the AI is on its own. You provide unlabeled data, and the model looks for hidden patterns, structures, or clusters without being told what to look for. • Common tasks: Customer segmentation, anomaly detection, recommendation engines. • Analogy: Giving a kid a pile of random blocks and letting them group them by color or shape without any instructions. The Bottom Line: • Supervised: Predicting known outcomes (Classification/Regression). • Unsupervised: Discovering hidden patterns (Clustering/Association). Which AI technology or model are you learning or implementing in your projects? #AI #MachineLearning #supervisedlearning #Unsupervisedlearning

We often hear that Machine Learning models learn patterns in data. But what does that look like in geometry? Picture dropping an elastic mesh into a cloud of points and letting it adapt. How would it bend, stretch, and settle so it matches the shape hiding in the data? In this scene we watch a self-organizing map(SOM)...a classic unsupervised neural model...learn a 2D dataset arranged like a spiral arm. Over the points we place a square grid of neurons whose weights live in the same plane. At the start it’s just a flat net drifting across the cloud. No clue what the structure is. Fir the SOM, learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, then nudge that neuron and its neighbours toward the point. Repeat, repeat, repeat...while gradually shrinking how wide the neighbourhood influence spreads. The result is satisfying...the grid stops being a grid and turns into a coordinate sheet wrapped onto the spiral. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML #SelfOrganizingMap #Topology

🎥 *Análise Multivariada e Aprendizado Não-Supervisionado* Esta é uma das playlists que mais amo do meu canal, de uma das minhas disciplinas favoritas também. Recomendo como requisito ter estudado Inferência Estatística univariada e Álgebra Linear💡❤️☀️ Aqui falo de Inferência Estatística Multivariada e técnicas de aprendizado de máquina estatístico não-supervisionado, incluindo estimação, testes de hipóteses, regressão linear multivariada, técnicas para redução de dimensionalidade, agrupamentos (clustering), análise discriminante linear, análises de associação e visualização de dados multivariados. Você já assistiu ou já usou as minhas aulas dessa playlist? Deixa pra mim nos comentários! 📌 **Assista a playlist aqui:** [youtube.com/playlist?list=PL…) Compartilhe o post, conteúdo gratuito e de qualidade para quem quer estudar Estatística e Ciência de Dados! 🚀 #Estatística #DataScience #AprendizadoDeMáquina #MachineLearning #UnsupervisedLearning #AnáliseMultivariada #Clustering #PCA #CiênciaDeDados

1/ I am thrilled to share that Shira Lifshitz will present our paper at AAAI (oral poster)! This was a collaborative effort with Ron Meir (EE, Technion), @gmishne (UC San Diego), and @Ofirlin (BIU). #AAAI #UnsupervisedLearning #FeatureSelection #GraphLearning #DataScience

Day 10/30 🚀 Completed a DBSCAN clustering project: • Customer segmentation • Outlier (high-value) detection Repo 👉 github.com/Sudhanshugochar/D… #MLJourney #MachineLearning #DataScience #UnsupervisedLearning #LearningInPublic

🧪 Day 9/30 of my ML journey Implemented Hierarchical Clustering on the Iris dataset 🌸 No labels. Just pure pattern discovery. GitHub Repo 👇 github.com/Sudhanshugochar/I… #MachineLearning #UnsupervisedLearning #BuildInPublic #DataScience #100DaysOfML

GBS-Assisted Quantum Unsupervised Machine Learning on a Universal Programmable Integrated Quantum Chip. #QuantumComputing #MachineLearning #QuantumAI #IntegratedPhotonics #UnsupervisedLearning #OpenAccess doi.org/10.34133/research.10…

💥Excited to share the publication: "Integrating #WearableSensor #SignalProcessing with #UnsupervisedLearning Methods for #Tremor Classification in #Parkinson’s Disease" 🔗 shorturl.at/jRbxj 🏫 @IrccsMe

In 1994 Bernd Fritzke took Martinetz & Schulten’s “neural gas” idea and made it grow. The model keeps inserting new nodes where the accumulated quantization error stays high, while a competitive Hebbian rule stitches an edge between the winner and runner-up, and old edges age out and vanish. This render is that mechanism in motion...a drifting 3D manifold keeps sliding under the camera, the data fog traces the surface, and the graph is forced to keep re-topologizing...nodes chase the moving mass, edges snap into the local geometry, then prune when they stop being reinforced. You don’t train to a fixed picture here; you watch a live topology tracker that can keep learning as the world changes. #MachineLearning #NeuralGas #GrowingNeuralGas #UnsupervisedLearning #ManifoldLearning #Topology

GUANinE v1.1 Reveals Complementarity of Supervised and Genomic Language Models 1. The GUANinE v1.1 benchmark provides a comprehensive evaluation of supervised and unsupervised genomic models, revealing that each excels in different tasks. Supervised models dominate functional annotation tasks like chromatin accessibility, while unsupervised models perform better in evolutionary conservation tasks. 2. The study introduces two new large-scale variant interpretation tasks: cadd-snv for measuring proxy deleteriousness and clinvar-snv for clinical pathogenicity. Conservation scores and language models dominate deleteriousness prediction, but translating this to pathogenicity remains challenging. 3. The research highlights that input context size and model parameter count trade off when compute budget is fixed. Models with larger context sizes don't always outperform those with fewer parameters, suggesting that parameter density and model complexity are crucial factors. 4. GUANinE v1.1 demonstrates that training on distal evolutionary sequences can improve model performance, even for human-specific tasks. This suggests that leveraging diverse genomic data can enhance the robustness of genomic language models. 5. The study also explores the potential of combining supervised and unsupervised approaches, showing that hybrid models may define the next era of genomic sequence modeling. An ensemble of NT-v2-500m and Sei outperformed either model alone on variant effect prediction tasks. 📜Paper: biorxiv.org/content/10.64898… #Genomics #MachineLearning #Benchmarking #GenomicLanguageModels #SupervisedLearning #UnsupervisedLearning

AI without human bias. Wallexus uses Unsupervised Intelligence to naturally discover wallet behavior. No "whales" or "retail" labels, just pure data. #UnsupervisedLearning #ZeroBiasAI #CryptoAnalytics #Web3

AI TEST 😀😀#AI #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #GenerativeAI #LLM #LargeLanguageModel #ChatGPT #Grok #xAI #OpenAI #AGI #ASI #NLP #NaturalLanguageProcessing #ComputerVision #ReinforcementLearning #SupervisedLearning #UnsupervisedLearning #Transformer #BERT #GPT #DiffusionModel #StableDiffusion #DALLE #Midjourney #Lora #FineTuning #PromptEngineering #RAG #VectorDatabase #Embedding #Token #ContextWindow #Hallucination #AIEthics #BiasInAI #ExplainableAI #XAI #Alignment #SuperAlignment #AutonomousAI #Robotics #AIArt #TextToImage #ImageGeneration #SyntheticData #Multimodal #ChainOfThought

In our previous post, we dropped a self-organizing map onto a single curved manifold and watched a neural sheet fold itself around a spiral-and-ring galaxy of points. Now, we make life harder: the data itself won’t sit still. In this scene, the SOM is the same elastic sheet, but the world underneath is drifting. The SOM doesn’t get reset between regimes. It has to carry its history while adapting to each new shape. The node trails record where the neurons have been in (x, y) space across all three phases. #MachineLearning #UnsupervisedLearning #SelfOrganizingMap #ManifoldLearning #ConceptDrift #DataManifold #Topology #DataGeometry #NeuralMaps