A high-bias, low-variance introduction to Machine Learning for physicists · HackerPapers discussions

A high-bias, low-variance introduction to Machine Learning for physicists — HN submission

Thu, 25 Apr 2024 15:28:49 GMT

A high-bias, low-variance introduction to Machine Learning for physicists

5 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

A high-bias, low-variance introduction to Machine Learning for physicists — mentioned by quanto

Sat, 17 Jun 2023 23:31:55 GMT

I am looking for a book and resource for (advanced) data science. I understand there are numerous ML books and articles discussing algorithms at all levels; there are also numerous introductory books on data science, e.g. how to call functions in sklearn. What I am looking for is a book on data exploration, data modeling, and prediction error analysis; with some level of rigor in both theory and implementation. Any recommendation for such a book or resource? DatabookV2 discussed here looks great for natural science + ML. It reminds me of this widely-cited paper A high-bias, low-variance…

Paper and discussion history. A mention is not an endorsement.

A high-bias, low-variance introduction to Machine Learning for physicists — HN submission

Tue, 16 Aug 2022 16:42:34 GMT

A high-bias, low-variance introduction to Machine Learning for physicists (2019)

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

A high-bias, low-variance introduction to Machine Learning for physicists — mentioned by ivan_ah

Thu, 16 Aug 2018 10:24:14 GMT

This is very, very, very good. Book is here: https://arxiv.org/abs/1803.08823 ____ My review: The authors provide a condensed summary of all central topics in machine learning. Topics include ML basics, ML theory, optimization algorithms, but also a detailed introduction to modern methods deep learning methods. Code examples and tutorials are provided as jupyter notebooks for each chapter [2]. The book uses three datasets (MNIST digit recognition, SUSY physics data, and simulated Nearest Neighbor Ising Model) as running examples throughout the book to help learners understand what different…

Paper and discussion history. A mention is not an endorsement.

A high-bias, low-variance introduction to Machine Learning for physicists — mentioned by orbifold

Sun, 15 Jul 2018 22:54:01 GMT

Whether you find something like this surprising largely depends on your prior knowledge. There are quite different sets of people that work with statistics, theoretical physicists are one of them. So a recent advance in one field (machine learning), will inevitably draw in the hyenas from other fields, especially if one of their favorite toys (building increasingly complicated high energy particle physics models) has been largely taken away from them given the recent zero results at the LHC. Which is how you get review articles like this one (https://arxiv.org/abs/1803.08823).

Paper and discussion history. A mention is not an endorsement.

A high-bias, low-variance introduction to Machine Learning for physicists — mentioned by drams

Thu, 26 Apr 2018 21:06:04 GMT

Yes; The following recent review article actually provides code samples: https://arxiv.org/abs/1803.08823 which use an open-source version of our software called 'paysage' (https://github.com/drckf/paysage). This has currently not been updated too recently, but we expect to put out a new update quite soon. The update will clean up code, docs, features, but might not yet contain the BEAM training code. The latter is pending some decisions about IP, etc.

Paper and discussion history. A mention is not an endorsement.

A high-bias, low-variance introduction to Machine Learning for physicists — HN submission

Mon, 26 Mar 2018 22:04:40 GMT

A high-bias, low-variance intro to ML for physicists

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.