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austinrochford.com | ||
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isaacslavitt.com
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| | | | | [AI summary] The provided text is a detailed tutorial on using scikit-learn for machine learning tasks, including data preprocessing, model selection, cross-validation, and pipeline creation. It also touches on integrating R and Julia with Python through Jupyter notebooks. | |
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twiecki.io
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| | | | | [AI summary] This technical blog post explains the advantages of hierarchical Bayesian modeling over non-hierarchical approaches using a case study of predicting radon levels across different US counties with the PyMC3 library. | |
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chipnetics.com
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| | | | | There is a lot to remember in data science! It touches everything from alignment, to data wranging, data analytics, storytelling and visuals. This python cheat sheet is a quick reference to get a fast boost into many of these areas. | |
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brenocon.com
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| | | [AI summary] The provided text is a collection of comments and discussions from a blog post that originally criticized artificial neural networks (ANNs) in 2008. The comments reflect a range of opinions and debates about the relationship between machine learning (ML) and statistics, with some users defending ML techniques like support vector machines (SVMs), probabilistic graphical models, and deep learning, while others argue for the importance of statistical methods. There are also discussions about the need for better communication between disciplines, the limitations of ML approaches, and the importance of understanding the underlying assumptions of models. The text includes recommendations for textbooks and courses, such as 'All of Statistics' and Andre... | ||
