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 www.jeremiecoullon.com
Early Monte Carlo methods - Part 1: the 1949 conference
| | kidger.site
Just know stuff. (Or, how to achieve success in a machine learning PhD.) · Patrick Kidger
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| | Personal website. Math, SciML, scuba diving!
| | erikbern.com
The hacker's guide to uncertainty estimates · Erik Bernhardsson
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| | I made a New Year's resolution: every plot I make during 2018 will contain uncertainty estimates. Nine months in and I have learned a lot, so I put together a summary of some of the most useful methods.
| | uminnpressblog.com
The Most Dangerous Book in the World - University of Minnesota Press Blog
4.3 parsecs away

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| | BY SETH PERLOWGeorgetown UniversityOne book, written by a computer, could have killed us all.What do you do when you're the only country in the world with atomic bombs? You make them much, much bigger. That was the US strategy right after World War II. The Cold War was beginning, and by 1952 the US would...
| | jaketae.github.io
Convolutional Neural Network with Keras - Jake Tae
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| Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I decided it was a good starting point. I have just finished the first section of Part 2 on Convolutional Neural Networks and image processing. My impression so far is that the book is more focused on code than math. The apparent advantage of this approach is that it shows readers how to build neural networks very transparently. It's also a good introduction to many neural network models, such as CNNs or LSTMs. On the flip side, it might leave some readers wondering why these models work, concretely and mathematically. This point notwithstanding, I've been enjoying the book very much so far, and this post is...