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The ideas contained in this book and Causality have been a huge influence in my decision of what to focus on learning as a data scientist. Very much recommend i
by darkxanthos 11y ago
The ideas contained in this book and Causality have been a huge influence in my decision of what to focus on learning as a data scientist. Very much recommend it even if you feel like the book might be too dense for you. It definitely was for me, but I still got a lot out of it.
- stuxnet79 11y agoOn the contrary I am a bit shocked that it is only 40 pages long (or is this a fragment of it)? This is on my to-read list since it is always getting referenced one way or another in the LessWrong Wiki, but I was putting off reading it because I figured it would be a bit dense.
- darkxanthos 11y agoThis is a fragment. There's no way either of these books are less than a couple hundred pages.
- ced 11y agoOut of curiosity, where did you go (what did you read) after these two books?
- darkxanthos 11y agoI took a step back and read Think Bayes by Downey and watched some of his youtube videos. Then Introduction to Bayesian Statistics by Bolstad is great once you're reading to deal with probabilities. Now I'm reading * Building probabilistic graphical models with Python (Karkera) * Mastering probabilistic graphical models using Python (Ankan) * Probabilistic Graphical Models Principles and techniques (Koller) Having skimmed (just started reading them) I'm very excited to continue to delve into them.
- ced 11y agoAh, I took a similar path - applied Bayes. I was wondering where to go next for theory after Causality. It was such an interesting book.
- darkxanthos 11y agoProbabilistic Graphical Models is a pretty good blend. Not sure how theoretical you want.