tullo.ch

Andrew Tulloch

Machine Learning, Statistics, Systems. Cambridge Part III Mathematics Notes. Ive cleaned up (somewhat) my notes from Cambridge Part III and have put them online - with LaTeX sources available on GitHub. And PDFs linked below. Mathematics Of Operations Research. Time Series and Monte Carlo Analysis. As far as I can tell, the LASSO estimator is the closest thing we have to a miracle in modern statistics. The Optimality of the LASSO estimator. The proof is fairly elementary, requiring only a basic concentra...

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Andrew Tulloch | tullo.ch Reviews

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Machine Learning, Statistics, Systems. Cambridge Part III Mathematics Notes. Ive cleaned up (somewhat) my notes from Cambridge Part III and have put them online - with LaTeX sources available on GitHub. And PDFs linked below. Mathematics Of Operations Research. Time Series and Monte Carlo Analysis. As far as I can tell, the LASSO estimator is the closest thing we have to a miracle in modern statistics. The Optimality of the LASSO estimator. The proof is fairly elementary, requiring only a basic concentra...

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2 academic

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4 advanced financial models

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6 summary

7 advanced probability

8 applied bayesian statistics

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10 non parametric statistics

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Andrew Tulloch | tullo.ch Reviews

https://tullo.ch

Machine Learning, Statistics, Systems. Cambridge Part III Mathematics Notes. Ive cleaned up (somewhat) my notes from Cambridge Part III and have put them online - with LaTeX sources available on GitHub. And PDFs linked below. Mathematics Of Operations Research. Time Series and Monte Carlo Analysis. As far as I can tell, the LASSO estimator is the closest thing we have to a miracle in modern statistics. The Optimality of the LASSO estimator. The proof is fairly elementary, requiring only a basic concentra...

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tullo.ch

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Academic — Andrew Tulloch

http://tullo.ch/academic

Machine Learning, Statistics, Systems. Im a member of the Facebook AI Research Group. FAIR), working on large-scale problems in machine intelligence. Im currently applying deep learning methods to tasks in computer vision and natural language processing. You can find a more informal and in-depth biographic at /about. You can get in touch with me at andrew@tullo.ch. Blog comments powered by Disqus.

2

LaTeX2Markdown Demonstration — Andrew Tulloch

http://tullo.ch/projects/LaTeX2Markdown

Machine Learning, Statistics, Systems. The following document was generated entirely by the LaTeX2Markdown. Tool See below for the generated Markdown and the source LaTeX. The PDF. Generated from the LaTeX document) are also available. The following is the entire LaTeX document used to generate this post. Blog comments powered by Disqus.

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About — Andrew Tulloch

http://tullo.ch/about

Machine Learning, Statistics, Systems. You can get in touch with me at andrew@tullo.ch. My academic interests are mostly machine learning, especially big learning - distributed and parallel methods applied to large-scale problems. Ive had experience implementing large-scale machine learning systems at Facebook, and Im comfortable working in C , Python, Go, R, Haskell, Rust, MatLab, and other languages. See my GitHub profile. For some of my open-sourced code - for example,. And some minor ones, such as.

4

Stripe CTF Distributed Systems - Minimal Solutions — Andrew Tulloch

http://tullo.ch/articles/stripe-ctf-golfing

Machine Learning, Statistics, Systems. Stripe CTF Distributed Systems - Minimal Solutions. Distributed Systems edition has just finished, and I passed all the levels (and was one of the first fifty contestants to finish). In constructing my solutions, I thought it would be an interesting challenge to attempt to construct the minimal changes to the reference solutions that are sufficient to pass the scoring requirements. Solutions, and somewhat interesting for that reason. The net diff is two lines changed.

5

Python vs Julia - an example from machine learning — Andrew Tulloch

http://tullo.ch/articles/python-vs-julia

Machine Learning, Statistics, Systems. Python vs Julia - an example from machine learning. In Speeding up isotonic regression in scikit-learn. We dropped down into Cython to improve the performance of a regression algorithm. I thought it would be interesting to compare the performance of this (optimized) code in Python against the naive Julia implementation. This article continues on from the previous one. The Cython code is available on GitHub at. We see that exactly the same algorithm in Julia is unifo...

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