I’m quite happy with my progress: more books read, with subjects like astronomy, history, time management and sci-fi. Let’s get to them.
Books and short stories
- みんなの日本語 1 / Minna no Nihongo 1 — amazon • amazon jp
- A Student’s Guide to the Mathematics of Astronomy — amazon
- At the Mountains of Madness — amazon (single story)
- Os gatos de Ulthar / The cats of Ulthar — amazon (single story)
- The Memory Palace of Matteo Ricci — amazon
- The Visual Display of Quantitative Information — amazon
- Time Management for System Administrators — amazon • o’reilly
- Snow Crash — amazon
- Elon Musk — cultura • saraiva • amazon
- Na Vida Real / In Real Life — skoob • amazon
- A Lição de Anatomia do Temível Dr. Louison — cultura • saraiva
- Vaporpunk — cultura • saraiva • amazon
I did the Japanese-Language Proficiency Test (JLPT) in December, thus #1 (I bought the second book of the series as well, but didn’t get to it on time). It’s a bit hard for self-study, but I didn’t get stuck nor anything.
A decision I should have taken long ago is to read more diverse material instead of concentrating on programming and mathematics: astronomy (#2), history (#4), and biographies (#8) are subjects I haven’t studied in a while. Not only that, but a lot of fiction including two Steampunk books (#10, #11) and the first graphic novel (#9) I’ve ever bought. Snow Crash impressed me. I should have read Neal Stephenson before.
Finally: everyone should (re-)read H.P. Lovecraft from time to time. I still experience a varied mix of emotions—fear, anxiety and a lot of curiosity—when reading At the Mountains of Madness. My edition of #4 was made by Breno Macedo with illustrations in watercolor; the work is magnificent. Please take a look at this gallery to see how beautiful it is.
- The Elements of Statistical Learning — amazon • pdf
- Pattern Recognition and Machine Learning, Bishop — amazon • pdf
- Machine Learning, Tom Mitchell — amazon • pdf
- Probabilidade: Um curso em nível intermediário / “Probability: An intermediate course” — IMPA
These books were mostly used during graduate classes. I really liked Tom Mitchell’s book, but it is a bit outdated in some aspects; his datasets aren’t really big anymore and the programs used in the examples can’t be easily compiled under modern systems due to updates in headers and libraries since the 90’s (most of them are available on the book’s website). Bishop’s book is also good, but I felt it was too tiresome.
The Elements of Statistical Learning uses a very sophisticated language—I have to look some things from Statistics while reading it. The content embraces several subjects in Machine Learning and I can see myself studying their treatment of some topics to compare with the common approaches in ML.
Book #4 teaches several probability concepts as formally as possible without recurring to Measure Theory (well, it says intermediate). I still need to go through Measure Theory before facing a more advanced book…
- Induction of Decision Trees. J.R. Quinlan. — pdf
- An Introduction to ROC Analysis. Tom Fawcett. — pdf
- Giraffe: Using deep reinforcement learning to learn to play chess. Matthew Lai. — arXiv
- Learning Deep Architectures for AI. Yoshua Bengio. — pdf (poor quality) • ACM Digital Library • slides • article
- Introductory physics: The new scholasticism. Sanjoy Mahajan, David W. Hogg. — arXiv
- Computing Machinery and Intelligence. Alan Turing. — pdf
- Lots of other papers on Deep Learning…
Some of these were really interesting, but I have to say that #3 was one of the most fun technical pieces I’ve read in some time. Someday, there should be a similar engine for playing Pokémon. By the way, re-reading #6—better yet, any of Turing’s papers—is always a good thing to do.
I organized my bookcase to leave enough room for every untouched book in my possession; I started writing a Reading List for 2016 that should be done in the next few days. Also, I will try to keep these posts monthly instead of quarterly in hopes of writing more about each thing I read.