an underrated skill in ML is deriving great insight from small wiggles in the graphs
MACHINE LEARNING
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Kirk Borne Offers Over 3600 Free Resources for Subscribers
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I continue adding more FREE resources for my @X Subscribers to access: ๐ผ๐๐ฒ๐ฟ ๐ฏ๐ฒ๐ฌ๐ฌ ๐ฟ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐ฎ๐ฟ๐ฒ ๐ป๐ผ๐ ๐ฎ๐๐ฎ๐ถ๐น๐ฎ๐ฏ๐น๐ฒ. Subscribe for only US$4.99/month #MachineLearning #ML #AI #DataScience #Python #Mathematics #Statistics #SQL #GenAI #AgenticAI
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Cool Algorithm Trick for AI IoT and Digital Transformation
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Cool Algorithm Trick โจ@InterestingSTEM #AI #IoT #5G #DigitalTransformation @Hal_Good @gvalan @enilev @Analytics_699 @Shi4Tech
— Harold Sinnott #MWC26 (@HaroldSinnott) 7 septembre 2025
pic.twitter.com/BcUuevLP9FCool Algorithm Trick @InterestingSTEM #AI #IoT #5G #DigitalTransformation @Hal_Good @gvalan @enilev @Analytics_699 @Shi4Tech
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Causal Substrate and Symbolic Representation in Models
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"Does a causal substrate necessarily need to be symbolic?" you ask. Could it be based on parametric curves? Yes it does, because in such a substrate the model is isomorphic to the graph of causal factors of what you are modeling. And such a graph is necessarily very sparse, i.e.
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Deep Learning Generalization: Interpolation Versus Causal Understanding
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When you store your knowledge and skills as parametric curves (as all deep learning models do), the only way you can generalize is via interpolation on the curve. The problem is that interpolated points *correlate* with the truth but have no *causal* link to the truth. Hence
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Machine Learning Algorithms: Supervised and Unsupervised Learning Quick Guide
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Machine Learning Algorithms โ Quick Guide Supervised Learning Regression โ Linear, Polynomial Decision Tree Random Forest Classification โ KNN, Logistic Regression, Naive Bayes, SVM Unsupervised Learning Clustering โ SVD, PCA, K-means Association
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Theoretical Limitations of Embedding-Based Dense Retrievers
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8. On the Theoretical Limitations of Embedding-based Retrieval Finds that single-vector dense retrievers cannot realize all possible top-k relevance combinations once queries demand sufficiently many โmix-and-matchโ document sets.
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Self-Evolving AI Agents: Continuous Adaptation Through Feedback
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9. Self-Evolving Agents This survey reviews techniques for building self-evolving AI agents that continuously adapt through feedback loops, bridging static foundation models with lifelong adaptability.
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Implicit Reasoning in LLMs: Multi-Step Problem Solving
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7. Implicit Reasoning in LLMs This survey defines implicit reasoning as multi-step problem solving that happens inside a modelโs latent states without printing intermediate steps.
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Brain-Like Representations Emerge in Self-Supervised Vision Transformers
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2. Disentangling the Factors of Convergence between Brains and Computer Vision Models
— DAIR.AI (@dair_ai) 7 septembre 2025
Large self-supervised ViTs trained on natural images develop brain-like internal representations.https://t.co/YBI1egjOHP2. Disentangling the Factors of Convergence between Brains and Computer Vision Models Large self-supervised ViTs trained on natural images develop brain-like internal representations.