Hacker News Books

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

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Ecology of the Planted Aquarium: A Practical Manual and Scientific Treatise for the Home Aquarist

Diana Walstad

4.7 on Amazon

4 HN comments

What the Dog Saw: And Other Adventures

Malcolm Gladwell and Hachette Audio

4.5 on Amazon

4 HN comments

Reinventing Your Life: The Breakthrough Program to End Negative Behavior and Feel Great Again

Jeffrey E. Young , Janet S. Klosko , et al.

4.6 on Amazon

4 HN comments

Entangled Life: How Fungi Make Our Worlds, Change Our Minds & Shape Our Futures

Merlin Sheldrake

4.8 on Amazon

4 HN comments

The Conversation: How Seeking and Speaking the Truth About Racism Can Radically Transform Individuals and Organizations

Robert Livingston

4.7 on Amazon

4 HN comments

The Upside of Stress: Why Stress Is Good for You, and How to Get Good at It

Kelly McGonigal

4.6 on Amazon

4 HN comments

Python Data Science Handbook: Essential Tools for Working with Data

Jake VanderPlas

4.6 on Amazon

4 HN comments

The House of the Scorpion

Nancy Farmer

4.6 on Amazon

4 HN comments

Operation Paperclip: The Secret Intelligence Program that Brought Nazi Scientists to America

Annie Jacobsen and Hachette Audio

4.7 on Amazon

4 HN comments

Proofs: A Long-Form Mathematics Textbook (The Long-Form Math Textbook Series)

Jay Cummings

4.7 on Amazon

4 HN comments

UFOs: Generals, Pilots, and Government Officials Go on the Record

Leslie Kean and John Podesta

4.5 on Amazon

3 HN comments

False Alarm: How Climate Change Panic Costs Us Trillions, Hurts the Poor, and Fails to Fix the Planet

Bjorn Lomborg

4.6 on Amazon

3 HN comments

How To Brew: Everything You Need to Know to Brew Great Beer Every Time

John J. Palmer

4.8 on Amazon

3 HN comments

Thinking Mathematically: Integrating Arithmetic & Algebra in Elementary School

Thomas P Carpenter , Megan Loef Franke, et al.

4.7 on Amazon

3 HN comments

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Foster Provost and Tom Fawcett

4.4 on Amazon

3 HN comments

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Sorted by relevance

Tarq0nonMar 3, 2019

Hands-on Machine Learning with Scikit-Learn and TensorFlow [1] is more ML focused, but highly recommended. Out of the three books (Python for Data Analysis and Python Data Science Handbook) I learned the most from this one by far.

[1] https://github.com/ageron/handson-ml

mongodudeonAug 12, 2017

Think Bayes and Python Data Science Handbook are a good starting point. Below is the list of free books to learn ML/AI

http://blog.paralleldots.com/data-scientist/list-must-read-b...

asicsponAug 13, 2020

See also "Python Data Science Handbook" by Jake VanderPlas, which uses Jupyter notebooks too.

[0] https://jakevdp.github.io/PythonDataScienceHandbook/

bellweather49onDec 19, 2018

While I was in general agreement with the article, I think the examples are
extreme; the author has swung from wanting to learn every Java GUI framework
to reading books about softtware design. There is a middle ground, which is
what I was expecting the author to describe. Here is my (utterly incomplete
and non-comprehensive) list of books to read:

- The C Programming Language
- Computer Networking: Principles, Protocols and Practice
- The Art of Unix Programming
- An Introduction to Beginning Linux Programming
- Sams Teach Yourself SQL in 24 Hours
- The Python Data Science Handbook
- Python Programming with OpenCV
- Speaking Javascript
- Scalable and Modular Architecture for CSS

Wade through that lot and you will have learned about C, UNIX/Linux, networking,
HTML CSS, Javascript, data science/machine learning, text processing, and
computer vision. I reckon that covers 90% of what gets posted on here.

While some of this seems quite specific, all of these books teach either
principles such as machine learning, or teach actual standards such as POSIX,
HTML etc. None of these are going out of fashion anytime soon, unlike the latest
GUI framework or virtual DOM library.

The last book in my list actually speaks to the broader issue of framework
use. The core takeaway of the book can be summarised as this: HTML is a tree
data structure, and clean CSS relies on namespacing CSS rules so that they
only apply to a specific branch of the tree
, so no `.menu` classes or the
like, which will probably end up applying to all sorts of branches. I think
if every front-end dev understood this, libraries like React would have had
far less appeal, as everyone would have been too busy writing lean, fast HTMl
and CSS sites to have the time to learn how to make complicated React-powered
static blogs with loading spinners. (As an aside, I think there would have been
less of a backlash with motherfuckingwebsites and brutalist design, as only a
little CSS can make a site much more usable, with almost no impact on load time,
but I think people have been scared off it by bad experiences).

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