Books that i found useful in building my knowledge and perspective towards ML and in general. Will keep updating...
In "How Not to Be Wrong," Jordan Ellenberg explains math isn't just about abstract ideas that don't matter in real life; it's actually a part of everything we do and influences the entire world around us.
In this book, Chip Huyen teaches you how to handle data and choose the right metrics for business problems. It covers automating model development and updates, setting up a system to monitor and fix issues in production, building a versatile ML platform, and creating responsible ML systems.
Frederick Mosteller, one of the most eminent statisticians, presents various engaging probability problems in this book. It is not only educational but also fun, with detailed solutions included for each problem.
In this practical guide, Martin Kleppmann helps you understand different technologies for processing and storing data by exploring their pros and cons. While software evolves, core principles stay the same. This book teaches software engineers and architects how to apply these principles in modern applications, understand and improve existing systems, make informed decisions about tools, and navigate trade-offs in consistency, scalability, and fault tolerance.