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 www.oranlooney.com
ML From Scratch, Part 5: Gaussian Mixture Models
| | blog.quipu-strands.com
Bayesian Optimization, Part 2: Acquisition Functions
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| | [AI summary] The text presents an extensive overview of Bayesian optimization techniques, focusing on their applications in black-box function optimization, including challenges and solutions such as computational efficiency, scalability, and integration with deep learning models. It also highlights key research contributions and references to seminal papers and authors in the field.
| | jaketae.github.io
A sneak peek at Bayesian Inference - Jake Tae
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| | So far on this blog, we have looked the mathematics behind distributions, most notably binomial, Poisson, and Gamma, with a little bit of exponential. These distributions are interesting in and of themselves, but their true beauty shines through when we analyze them under the light of Bayesian inference. In today's post, we first develop an intuition for conditional probabilities to derive Bayes' theorem. From there, we motivate the method of Bayesian inference as a means of understanding probability.
| | sebastianraschka.com
Predictive modeling, supervised machine learning, and pattern classification
1.4 parsecs away

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| | I'm Sebastian: a machine learning & AI researcher, programmer, and author. As Staff Research Engineer Lightning AI, I focus on the intersection of AI research, software development, and large language models (LLMs).
| | akosiorek.github.io
Normalizing Flows
15.3 parsecs away

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| Machine learning is all about probability.To train a model, we typically tune its parameters to maximise the probability of the training dataset under the mo...