‘Read, Reading, Read Again’
Some writing that has stuck with me
I was recently asked for a list of things that have grabbed my attention recently, stick in memory from the past, and things I return to for re-reading. I’m not sure anyone cares about my opinions on books, etc. but here goes :)
Please recommend things to read in the comments!
Memorable literature from the past
Sci-fi informed my taste in books (for better or worse). The first lightning moment for me came from Philip Dick’s A Handful of darkness. Neuromancer (William Gibson), Snow crash (Neal Stephenson), Three body problem (Liu Cixin) shaped my thinking about the internet and just ooze an especially virulent kind of post-punk cool.
Fiction-wise, Donna Tartt, Jonathan Franzen & Cormac McCarthy novels are all achingly beautiful. Dharma bums by Jack Kerouac and Thomas Pynchon’s V and Against the Day inspired my love of travel and eventual move to California.
Down and out in Paris and London and Keep the Aspidistra flying by George Orwell remind me to not get too hung up about money, success and the rat race.
Useful & current stuff
Tim Urban’s Wait but why? blog is amazing. I aspire to write as lucidly and engagingly. They did a great piece on AI in 2015.
Neil also does an awesome podcast on machine learning with Katherine Gorman. It’s called Talking Machines. Reid Hoffman’s Masters of Scale podcast is super-inspiring, very well-produced and a great crash course in Silicon Valley thinking. To take the edge off that, Tim Ferris’ recent interview with Eric Ripert is packed with good, practical advice.
Recently I read Sapiens: A Brief History of Humankind by Yuval Noah Harari. The narrative of our species is ambitious topic to say the least, and the writing is thrilling, straight-forward and thought-provoking. I’m looking forward to diving into the follow-up Homo Deus: A Brief History of Tomorrow.
The circle by Dave Eggers should probably be required reading for anyone on the internet today. I’m currently reading Kingdom of fear by the inimitable Hunter S. Thompson. It was written in the aftermath of 2001’s terror attacks and its blend of paranoia, anxiety and outrage feels extremely relevant.
Things I return to
Despite the hype, it most often happens that some algorithm or approach that isn’t deep learning is what you need to solve a problem. I often refer to The Algorithm Design Manual by Steven Skiena. The war stories are fascinating, the writing is approachable and if nothing else, it is a constant reminder of why graphs are beautiful.
The scikit-learn documentation is an amazing resource for common machine learning tasks. There’s a book for deep learning with a creative name, by Goodfellow, Bengio and Courville. Bayesian methods get a bad rap for their difficulty. These two resources make it easy to get started: Probabilistic Programming and-Bayesian Methods for Hackers by Cam Davidson-Pilon and the Jupyter notebook version of Allen Downey’s Think Bayes by Roger Labbe.
I probably forgot loads of stuff, but I hope this list is at least useful to someone.