Hacker News Books

40,000 HackerNews book recommendations identified using NLP and deep learning

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Destiny: The Official Cookbook

Victoria Rosenthal

4.9 on Amazon

1 HN comments

Marketing Made Simple: A Step-by-Step StoryBrand Guide for Any Business

Donald Miller, Dr. J.J. Peterson, et al.

4.8 on Amazon

1 HN comments

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Gareth James , Daniela Witten , et al.

4.8 on Amazon

1 HN comments

Head First Design Patterns: Building Extensible and Maintainable Object-Oriented Software 2nd Edition

Eric Freeman and Elisabeth Robson

4.7 on Amazon

1 HN comments

The Internet of Money

Andreas M. Antonopoulos, Stephanie Murphy, et al.

4.6 on Amazon

1 HN comments

Ask Your Developer: How to Harness the Power of Software Developers and Win in the 21st Century

Jeff Lawson and Eric Ries

4.6 on Amazon

1 HN comments

How to DeFi

CoinGecko , Darren Lau , et al.

4.4 on Amazon

1 HN comments

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Sebastian Raschka and Vahid Mirjalili

4.5 on Amazon

1 HN comments

Math for Programmers: 3D graphics, machine learning, and simulations with Python

Paul Orland

4.9 on Amazon

1 HN comments

The Book of R: A First Course in Programming and Statistics

Tilman M. Davies

4.5 on Amazon

1 HN comments

The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution

Walter Isaacson, Dennis Boutsikaris, et al.

4.6 on Amazon

1 HN comments

Terraform: Up & Running: Writing Infrastructure as Code

Yevgeniy Brikman

4.6 on Amazon

1 HN comments

Uncanny Valley: A Memoir

Anna Wiener

4 on Amazon

1 HN comments

Web Scalability for Startup Engineers

Artur Ejsmont

4.8 on Amazon

1 HN comments

Ghost in the Wires: My Adventures as the World’s Most Wanted Hacker

Kevin Mitnick, William L. Simon, et al.

4.6 on Amazon

1 HN comments

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wenconJune 25, 2021

* Fooled By Randomness (NN Taleb): Taleb is a complicated personality, but this book gave me a heuristic for thinking about long-tails and uncertain events that I could never have derived myself from a probability textbook.

* Designing Data Intensive Applications (M Kleppmann): Provided a first-principles approach for thinking about the design of modern large-scale data infrastructure. It's not just about assembling different technologies -- there are principles behind how data moves and transforms that transcend current technology, and DDIA is an articulation of those principles. After reading this, I began to notice general patterns in data infrastructure, which helped me quickly grasp how new technologies worked. (most are variations on the same principles)

* Introduction to Statistical Learning (James et al) and Applied Predictive Modeling (Kuhn et al). These two books gave me a grand sweep of predictive modeling methods pre-deep learning, methods which continue to be useful and applicable to a wider variety of problem contexts than AI/Deep Learning. (neural networks aren't appropriate for huge classes of problems)

* High Output Management (A Grove): oft-recommended book by former Intel CEO Andy Grove on how middle management in large corporations actually works, from promotions to meetings (as a unit of work). This was my guide to interpreting my experiences when I joined a large corporation and boy was it accurate. It gave me a language and a framework for thinking about what was happening around me. I heard this was 1 of 2 books Tobi Luetke read to understand management when he went from being a technical person to CEO of Shopify. (the other book being Cialdini's Influence). Hard Things about Hard Things (B Horowitz) is a different take that is also worth a read to understand the hidden--but intentional--managerial design of a modern tech company. These some of the very few books written by practitioners--rather than management gurus--that I've found to track pretty closely with my own real life experiences.

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