📊 Optimization

Gradient Descent, Convex Optimization, Stochastic Methods, Loss Functions

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Optimality-Informed Neural Networks for Solving Parametric Optimization Problems
arxiv.org·3d
📊Dynamic Programming
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Error Bounds in a Smooth Metric for Brownian Approximation of Dynamical Systems via Stein’s Method | Journal of Statistical Physics
link.springer.com·1d
💃Dancing Links
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How to Train Ultralytics YOLOv8 models on Your Custom Dataset | 196 classes
reddit.com·8h·
Discuss: r/programming
🤖TVM
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Regression by Composition
lesswrong.com·1d
🧠Machine Learning
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Treating Functions as Vectors in Hilbert Space
hackaday.com·1d
📐Linear Algebra
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Deep Graph Contrastive Representation Learning
paperium.net·41m·
Discuss: DEV
🎨Chroma
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The 2025 Guide to Machine Learning
ibm.com·1d·
Discuss: Hacker News
🧠Machine Learning
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Advancing quantum imaging through learning theory
nature.com·14h
⚛️Quantum Computing
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Basics of Reinforcement Learning for LLMs
cameronrwolfe.substack.com·13h·
Discuss: Substack
🎯Reinforcement Learning
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No, Small Models Are Not the "Budget Option" (English)
mostlylucid.net·1h
🦙Ollama
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Gradient descent optimization algorithms in machine learning
dev.to·4d·
Discuss: DEV
🤖TVM
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<p>**Abstract:** The increasing complexity of clinical trial data, particularly the integration of multi-omics (genomics, transcriptomics, proteomics, metabolom...
freederia.com·1d
🧠Machine Learning
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Training a Model on Multiple GPUs with Data Parallelism
machinelearningmastery.com·1d
🔥PyTorch
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Dynamic graph neural networks for improving the reliability of traffic flow prediction: Progress and prospects
sciencedirect.com·1d
🕸️Graph Theory
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Building a Neural Network from scratch
pub.towardsai.net
·2d
📱Edge AI
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Stanford CS 224N | Natural Language Processing with Deep Learning
web.stanford.edu·1d
📝NLP
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Chapter 2 The Targeted Learning Roadmap
tlverse.org·1d
🧠Machine Learning
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A Tutorial on Bayesian Optimization
paperium.net·3d·
Discuss: DEV
💬Prompt Engineering
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Running Code and Failing Models – Rajiv Shah
projects.rajivshah.com·1d
📱Edge AI
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Distributionally Robust Neural Networks for Group Shifts: On the Importance ofRegularization for Worst-Case Generalization
dev.to·19h·
Discuss: DEV
📱Edge AI
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