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

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Active Measures: The Secret History of Disinformation and Political Warfare

Thomas Rid, Derek Perkins, et al.

4.5 on Amazon

3 HN comments

The Kubernetes Book: Updated April 2021

Nigel Poulton

4.4 on Amazon

2 HN comments

Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition

Brett Lantz

4.6 on Amazon

2 HN comments

Enchanted: A History of Fantasy Illustration

Jesse Kowalski, Rusty Burke, et al.

4.6 on Amazon

2 HN comments

Head First JavaScript Programming: A Brain-Friendly Guide

Eric Freeman and Elisabeth Robson

4.6 on Amazon

2 HN comments

Python Programming Language

Berajah Jayne

4.6 on Amazon

2 HN comments

The Site Reliability Workbook: Practical Ways to Implement SRE

Betsy Beyer , Niall Richard Murphy , et al.

4.7 on Amazon

2 HN comments

Python Programming: An Introduction to Computer Science, 3rd Ed.

John Zelle

4.6 on Amazon

2 HN comments

Unit Testing Principles, Practices, and Patterns: Effective testing styles, patterns, and reliable automation for unit testing, mocking, and integration testing with examples in C#

Vladimir Khorikov

4.8 on Amazon

2 HN comments

Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

Stefan Jansen

4.5 on Amazon

2 HN comments

Decode and Conquer: Answers to Product Management Interviews

Lewis C. Lin

4.5 on Amazon

2 HN comments

Hands-On Programming with R: Write Your Own Functions and Simulations

Garrett Grolemund and Hadley Wickham

4.5 on Amazon

2 HN comments

The Alignment Problem: Machine Learning and Human Values

Brian Christian and Brilliance Audio

4.6 on Amazon

2 HN comments

Building Mobile Apps at Scale: 39 Engineering Challenges

Gergely Orosz

5 on Amazon

2 HN comments

Learning Web Design: A Beginner's Guide to HTML, CSS, JavaScript, and Web Graphics

Jennifer Robbins

4.6 on Amazon

2 HN comments

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andyxoronApr 20, 2021

finance book do help, 'ML for finance' books rarely do, the domain is too new and attracts many charlatans.

You're better off just learning ML from its classics like Hastie & Tibshirani , Tom Mitchell, or Bishop PRML.

And learn finance from its own classics you can find in any "financial engineering" curriculum.

There is one I liked though because of hands-on approach:

"Machine Learning for Algorithmic Trading" https://www.amazon.com/Machine-Learning-Algorithmic-Trading-...

just assume its listed "strategies" are a sort of primitive "hello world"

akg_67onJune 7, 2021

/r/algotrading

- Trading Evolved, Andreas F. Clenow

- Systematic Trading, Robert Carver

- Developing & Backtesting Systematic Trading Strategies, Brian Peterson

- Algorithmic Trading, Ernest P. Chan

- Algorithmic Trading and DMA, Barry Johnson

- Trading Systems, Emilio Tomasini & Urban Jaekle

- Evidence-Based Technical Analysis, David R. Aronson

- Machine Learning for Algorithmic Trading, Stefan Jansen

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