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dagshub.com | ||
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distill.pub
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| | | | | How to tune hyperparameters for your machine learning model using Bayesian optimization. | |
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www.altexsoft.com
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| | | | | A dive into the machine learning pipeline on the production stage: the description of architecture, tools, and general flow of the model deployment. | |
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sebastianraschka.com
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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). | |
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easystats.github.io
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| | | You probably already have heard of the parameters package, a light-weight package to extract, compute and explore the parameters of statistical models using R (if not, there is a related publication introducing the package's main features). In this post, we like to introduce a new feature that facilitates nicely rendered output in markdown or HTML format (including PDFs). This allows you to easily create pretty tables of model summaries, for a large variety of models. | ||
