📚 Your Reading List for 2022
We have a few suggestions for you
📕📒📗 Happy holidays dear friends! In case you were planning your reading lists for the next year or a gift for a friend, we put together a few recommendations from the ML practitioners and entrepreneurs we talked to this year. Enjoy the reading and share this list if you like it! 💜
📚 Reading list
Two books were recommended a few times, so we put them on top of the list:
What else you might want to pay attention to:
“A Thousand Brains: A New Theory of Intelligence by Jeff Hawkins has to be one of the most fascinating AI books I have read in recent years, challenging some of the core foundations of AI and proposing alternative paths,” says Jesus Rodriguez, the founder of TheSequence, in his weekly editorial.
“Many people can suggest practically useful books for aspiring data scientists, I would actually suggest a book I found inspiring albeit not directly applicable.
One is "Who owns the future?" by Jaron Lanier, which made me think deeply about the economical, ethical, and societal implications of my work.
The other one is "Why greatness cannot be planned" by my former coworkers and good friends Ken Stanley and Joel Lehman. It is a thought-provoking take on our society's obsession with metric measurement and optimization of goals that are ill-defined. It all started from an unexpected discovery at their work in artificial intelligence,” says Piero Molino, creator of Ludwig.ai
“Invisible Women: Data Bias in a World Designed for Men by Caroline Criado Perez,” says Justin Harris, Senior Software Developer at Microsoft
“For someone new to the field, aspiring for a rigorous text – I would recommend the Pattern Classification book by Duda, Hart, and Stork. I am also a big fan of The Book of Why by Judea Pearl, especially the first chapter is super insightful,” says Krishna Gade, CEO of Fiddler AI
“Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron,” says Jim Dowling, CEO of Logical Clocks
“I always recommend The Elements of Statistical Learning (you can read it here for free). It has the right level of details and is amazingly well written,” says Jan Beitner, Creator of PyTorch Forecasting
“All of Statistics is my favorite 101 + reference book. It gives very applied, but rock-solid first principles intuition about data and statistics, which are the core of ML. A colleague on my ML team at Google lent me this and I read every page and did every exercise. It's dense, but you won't regret any ounce of effort you put into it,” says Mike Del Balso, CEO of Tecton
“Linear Algebra and Analytic Geometry by Ilyin and Kim, which is one of the first books you study as a mathematics faculty student at Moscow State University.
Overall, I think you should read as much literature on the sciences as possible. Some of it will fit your perceptions, and some will challenge them,” says Iskandar Sitdikov, ML Solutions Architect at Provectus
“There are many great technical books on ML, but I’d also encourage them to read some of the new books highlighting the societal impact of these systems such as Weapons of Math Destruction, Automating Inequality, and Race After Technology,” says Adam Wenchel, CEO of Arthur AI
“PRML (Pattern Recognition and Machine Learning) by Christopher Bishop, but I’ve seen people (including myself, to be honest!) find Deep Learning focused books more interesting than those on classical machine learning,” says Hyun Kim, CEO of Superb AI
“As you know, I always want to align data science with business value. There are many books on this subject, but one that I enjoyed is Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions by Matt Taddy,” says H.O. Maycotte, CEO of Molecula
“There are some obvious choices that are fun and go deep into NLP theory, like Jacob Eisenstein's Introduction to NLP. More about general ML in a practical and pragmatic way is Deep Learning for Coders by Jeremy Howard and Sylvain Gugger.
For engineers who want to study the critical peripheral components around ML, such as data processing at scale and working with data in general, I recommend: Designing Data-Intensive Applications by Martin Kleppmann and Computing with Data by Guy Lebanon and Mohamed El Geish are excellent in that regard,” says Emad Elwany, CTO at Lexion
“I recommend “An Introduction to Statistical Learning: with Applications in R” by Gareth James, Daniella Witten, Trevor Hastie, and Robert Tibshirani and Statistical Rethinking by Richard McElreath,” says Greg Finak, CTO of Ozette
“If you want to go hardcore, there are always Pattern Recognition and Machine Learning and The Elements of Statistical Learning. They are definitely not for binge reading, but they can occasionally be used as references to better understand the fundamentals,” says Rinat Gareev, Senior ML solutions architect at Provectus
“The Toloka team is full of great ML engineers, so I decided to ask them for the best advice. Our team recommends Introduction to Machine Learning with Python: A Guide for Data Scientists and Machine Learning Engineering,” says Olga Megorskaya, CEO of Toloka
“Weapons of Math Destruction by Cathy O'Neil, although my own views on predictive modeling are more optimistic,” says Doug Downey, research manager at Semantic Scholar
Hope you’ll find something for yourself on this list. Merry Christmas and Happy New Year! Don’t hesitate to follow us on Twitter, we share tons of helpful links there.