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austinrochford.com | ||
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weisser-zwerg.dev
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| | | | | Monte Carlo funamental concepts. | |
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gregorygundersen.com
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| | | | | [AI summary] Hamiltonian Monte Carlo (HMC) is a Markov Chain Monte Carlo (MCMC) method that leverages Hamiltonian dynamics to generate samples from a probability distribution. Unlike traditional MCMC methods that rely on random walks, HMC introduces auxiliary momenta variables and simulates a physical system to produce correlated samples with higher efficiency. The method uses gradient information of the log density to guide the sampling process, enabling faster exploration of the target distribution and higher acceptance rates. The implementation of HMC involves defining the potential and kinetic energy functions, performing leapfrog integration to approximate the Hamiltonian dynamics, and using the Metropolis-Hastings acceptance criterion. An example using... | |
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twiecki.io
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| | | | | [AI summary] This blog post discusses hierarchical linear regression in PyMC3, highlighting its advantages over non-hierarchical Bayesian modeling. The author explores how hierarchical models can effectively handle multi-level data by leveraging the 'shrinkage-effect', which improves predictions by borrowing strength from related groups. Using the radon dataset, the post compares individual and hierarchical models, demonstrating that the hierarchical approach provides more accurate and robust estimates, especially in cases with limited data. The key takeaway is that hierarchical models balance individual and group-level insights, offering the best of both worlds in data analysis. | |
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blog.paperspace.com
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| | | Follow this tutorial to learn what attention in deep learning is, and why attention is so important in image classification tasks. We then follow up with a demo on implementing attention from scratch with VGG. | ||
