The Divine Comedy

Dante Alighieri, translation by Mark Musa

Hardship and Happiness

Seneca, Lucius Annaeus, approximately 4 B.C.-65 A.D., author.; Fantham, Elaine; Hine, Harry M.; Ker, James, 1970-; Williams, Gareth D.

Deep Learning

Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Machine Learning System Design Interview: An Insider's Guide

Alex Xu and Ali Aminian

Stories of Your Life and Others

Ted Chiang

grokking algorithms

Aditya Y. Bhargava

Weapons of Math Destruction

Kathy O'Neil

A critical look at how big data and algorithms can reinforce inequality and harm society.

Review: An interesting exploration of the ethical implications of algorithms and data-driven decision making circa 2016. Creating fair algorithms can be extremely challenging, and the present day importance of fairness is exacerbated by recent boom in AI technologies. This book suffers slightly from "self-help book syndrome" where the author has a few excellent points, but needs padding to fill out the rest of the book.

I found the discussion on algorithms used in the hiring process, such as applicant tracking systems, to be particularly depressing. I would like to think that hiring practices has changed since 2016, but with the modern day prevalence of AI, I'm filled with a sense of dread.

I think since this books inception, there has been a rapid increase in the ways our data is automatically scraped and analyzed. Data broking seems to be more profitable than ever, and I'm worried about how vulnerable populations are being exploited. For example, it seems like every time I try shipment tracking, I receive several scam text messages telling me about how my package can't be delivered. These messages are very convincing, and likely fool many people.

Clean Code

by Robert C. Martin

This book emphasizes the importance of writing clean, maintainable code and provides guidelines for achieving it.

Review: An excellent read that has made me quickly realize how much my coding can improve. I think the suggestions for refactoring and improving readability are well supported and thoughtfully explained. Getting used to reading Java was headspinning, but the lessons are still valuable. I think reviews calling this book "life-changing" and "transformative" are well deserved. I strongly recommend this book to anyone looking to improve their coding skills.

Machine Learning Interviews

by Susan Shu Chang

Preparation strategies for machine learning interviews, including technical skills and critical thinking.

Thoughtful Machine Learning with Python: A Test-Driven Approach

by Matthew Kirk

A practical guide to machine learning with Python, focusing on best practices and real-world applications.

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

by Chip Huyen

Practical guidance on designing and building machine learning systems.

The Hundred-Page Machine Learning Book

by Andriy Burkov

A concise and practical guide to machine learning, covering essential concepts and techniques.

The StatQuest Illustrated Guide To Machine Learning

by Josh Starmer

TRIPLE BAM!!! An illustrated guide to machine learning concepts, making complex topics accessible and engaging.

Statistics Done Wrong

by Alex Reinhart

A guide to the most popular statistical errors and slip-ups committed by scientists every day, in the lab and in peer-reviewed journals.