datamlistic
@datamlistic
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Explaining machine learning in simple terms on my YouTube channel.
Joined November 2022
Eigenvalues and eigenvectors sound complicated, but they unlock the way matrix transformations really work. I just released a clear, practical explanation with a full example—perfect for linear algebra, ML, and data science. Watch here 👉
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Understanding why variance uses n-1 changes how statistics feels—Bessel’s correction is more intuitive than it sounds. 👉 Full video link: https://t.co/h7o0bfk53Q
#statistics #math #datascience #learning
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Gradient boosted trees power many of today’s best ML models, and I created a simple explanation showing how boosting builds strong predictions step by step. Watch full video here 👉 https://t.co/kNnYLj5jjj
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Just released a clear and practical explanation of XGBoost — one of the most powerful algorithms in machine learning. If you want stronger models and better accuracy, this will help a lot. Watch here 👉 https://t.co/KtABcdJj31
#MachineLearning #AI #DataScience #XGBoost
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Just released a new video explaining how Gaussian Mixture Models and Expectation–Maximization work together to create powerful probabilistic clustering. If you’re learning ML, this one is super helpful. 👉 Watch here: https://t.co/eVLfo9KHzq
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Just released a new video exploring Z-Image, a 6B model that rivals giant 20B–80B image generators—while running on consumer hardware. Efficient data curation, single-stream diffusion, fast 8-step sampling, and strong editing abilities. Watch here 👉
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Understanding least squares becomes effortless once the link to the Gaussian distribution clicks. I just released a new video showing exactly why this connection matters in stats, ML, and model fitting. Watch here 👉 https://t.co/hWKBUnhBtg
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New video out now! Bagging vs Boosting explained in a clear and practical way — learn how each method improves accuracy, controls errors, and strengthens ML models. Watch here 👉 https://t.co/Cs03uUijt3
#MachineLearning #AI #DataScience
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Just released a new video explaining conditional probability in a simple and intuitive way. If P(A|B) ever felt confusing, this clears it up fast. 🎥 Watch here: https://t.co/LJCun2B8Yh
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Just released a new video explaining Gaussian Mixture Models — how they work, why they matter, and how they cluster data using probabilities. A clean and intuitive guide for anyone learning ML. Watch here 👉 https://t.co/eVLfo9LfoY
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LightRAG introduces a fast and scalable way to bring graph structure into RAG without the heavy costs of previous graph-based methods. 🚀 Clear retrieval, cheap updates, and strong benchmark results. 📺 Watch the full explanation: https://t.co/EZF1JrXxBM
#AI #RAG #LLM
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New video just released! A clear and intuitive explanation of marginal probability—what it is, why it matters, and how it’s computed in statistics and machine learning. Full video here 👉 https://t.co/LJCun2B8Yh
#probability #statistics #datascience #machinelearning
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Stop solving linear systems the slow way. LU Decomposition is the shortcut. It factors a matrix A into L (Lower) and U (Upper) components, turning a complex problem into two simple steps. If you use numerical methods, you need this concept. 📽️Full video: https://t.co/hxTu4aIZtz
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Just released a new video explaining how the Baum-Welch algorithm actually learns a Hidden Markov Model from raw observations. If concepts like EM, Forward-Backward, transitions, or emissions ever felt mysterious, this will clear it up fast. Watch here:
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Understanding triangular matrices can make many linear algebra problems much easier. Here’s a clear explanation of their key properties and why they matter. 📺 Watch the full video: https://t.co/hxTu4aIrE1
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🚀 New video just dropped! I explain why predicting clean images instead of noise changes everything for diffusion models. A huge shift in how generative models should be built. ▶️ Watch here: https://t.co/PQRRgatDnv
#AI #MachineLearning #DiffusionModels #GenerativeAI
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Understanding joint probability can change the way you look at data. Here’s a clear explanation of how events interact and how joint distributions really work 👇 🎥 Watch the full video: https://t.co/LJCun2B8Yh
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The real story of Meta's SAM-3D is its Data Engine. Insight: Stop asking humans to create 3D meshes. Ask them to RANK candidate models. This "Verification-over-Generation" pipeline is how they solved the 3D data problem. 🔥 https://t.co/SWJ85wwBXk
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Understanding text starts with the right tokenizer. Here’s a clear look at SentencePiece—how it works and why LLMs rely on it today. Watch the full explanation 👇 🔗 https://t.co/5tceDnBln5
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Stop chasing complex 3D architectures. 🛑 The new State-of-the-Art in 3D Geometry Estimation uses a single, plain transformer and a minimal "Depth Ray" target. Full paper breakdown:
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