The most enjoyable and useful Mathematical books of all time

Terezija Semenski — Tue, 26 May 2026 13:57:10 +0000

During the years, I developed a routine of reading a lot of books (especially books about mathematics) so taking into account my own experience and the experience of many peers and learners I've spoken with (and who loved the list I'm sharing with you), I compiled a list of the most enjoyable and useful math books that every curious person should read (even if you struggle with math anxiety):

𝟭. 𝗙𝗲𝗿𝗺𝗮𝘁'𝘀 𝗟𝗮𝘀𝘁 𝗧𝗵𝗲𝗼𝗿𝗲𝗺

One of the greatest mathematical adventure stories ever told. Simon Singh traces the 350-year mystery sparked by Fermat's note in the margin and follows Andrew Wiles's astonishing, decade-long effort to finally prove it.

𝟮. 𝐓𝐡𝐞 𝐌𝐚𝐧 𝐖𝐡𝐨 𝐋𝐨𝐯𝐞𝐝 𝐎𝐧𝐥𝐲 𝐍𝐮𝐦𝐛𝐞𝐫𝐬: 𝐓𝐡𝐞 𝐒𝐭𝐨𝐫𝐲 𝐨𝐟 𝐏𝐚𝐮𝐥 𝐄𝐫𝐝ő𝐬 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐒𝐞𝐚𝐫𝐜𝐡 𝐟𝐨𝐫 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐚𝐥 𝐓𝐫𝐮𝐭𝐡

A brilliant biography of Paul Erdős, one of the most prolific mathematicians ever. Packed with stories of eccentricity, collaboration, and pure mathematical passion, this book shows the deeply human side of mathematics.

𝟯. 𝗧𝗵𝗲 𝗦𝗶𝗺𝗽𝘀𝗼𝗻𝘀 𝗮𝗻𝗱 𝗧𝗵𝗲𝗶𝗿 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗦𝗲𝗰𝗿𝗲𝘁𝘀

The author Simon Singh uncovers the surprising amount of math hidden inside The Simpsons and Futurama, thanks to writers with math PhDs. A fun, approachable journey through number theory, geometry, and more.

𝟰. 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝘁𝗵𝗲 𝗙𝘂𝗻 𝗪𝗮𝘆

This is an excellent book for getting an intuitive and gentle introduction to Bayesian thinking, using playful examples. One of the most enjoyable and accessible entry points into probability.

𝟱. 𝗜𝗻 𝗖𝗼𝗱𝗲: 𝗔 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗝𝗼𝘂𝗿𝗻𝗲𝘆

It is the easiest read of all the books I've listed here, but the most personal one. This part-memoir, part-math-journey follows a young girl, Sarah Flannery's rise from a curious Irish schoolgirl to an award-winning young mathematician.

𝟲. 𝗧𝗵𝗲 𝗠𝗮𝗻 𝗪𝗵𝗼 𝗞𝗻𝗲𝘄 𝗜𝗻𝗳𝗶𝗻𝗶𝘁𝘆

A powerful biography of Srinivasa Ramanujan, the self-taught genius whose work continues to amaze mathematicians. A deeply moving story of intuition, hardship, and brilliance.

𝟳. 𝐄𝐮𝐥𝐞𝐫'𝐬 𝐏𝐢𝐨𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐄𝐪𝐮𝐚𝐭𝐢𝐨𝐧: 𝐓𝐡𝐞 𝐦𝐨𝐬𝐭 𝐛𝐞𝐚𝐮𝐭𝐢𝐟𝐮𝐥 𝐭𝐡𝐞𝐨𝐫𝐞𝐦 𝐢𝐧 𝐦𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬

The author Robin Wilson explains why e^(iπ) + 1 = 0 is considered the most beautiful equation in mathematics. Step by step, you get introduced to counting systems, types of numbers, geometry, infinite series, and even complex numbers.

Check the full list (12 books + honorable mentions) in my free newsletter here:

https://mathmindset.substack.com/p/the-most-enjoyable-and-useful-mathematical

The 10 free books I recommend to every engineer learning ML math (and why order matters)

Terezija Semenski — Wed, 06 May 2026 14:24:20 +0000

After teaching hundreds of engineers learn machine learning last 5 years, a pattern becomes hard to ignore.

Most people don’t struggle because machine learning is too difficult.

struggle because they start in the wrong place.

The usual path looks like this:

Take a crash course.

Import a framework.

Train a model.

Tune hyperparameters and move on.

They start with tools.

Frameworks. APIs. Pretrained models.
Everything works, until it doesn’t.

At first, progress feels fast.

You can reproduce a tutorial in an evening. You can train a model in an afternoon. You can get something into production surprisingly quickly.

And then, slowly, friction appears.

A model overfits.
A small data change breaks performance.
A colleague asks why a method works better than another.
A paper introduces a “simple” idea that somehow feels impossible to follow.

At that moment, many engineers quietly conclude:

“I’m not a math person.”
That conclusion is wrong.

What’s actually missing is structure.

**Machine learning is not a collection of tricks.

Linear algebra, probability, statistics, and optimization are not “prerequisites.”**

They are the language machine learning is written in.

Once that language is familiar, many things that seemed complex become obvious.

When you skip those layers, everything above them feels fragile and mysterious.

This is why so many ML practitioners:

Can train models but can’t explain them
Can follow tutorials but can’t adapt ideas
Can use tools but struggle to reason about failure modes

The solution is not learning more frameworks, libraries and tools.

It’s better foundations.

And contrary to popular belief, you don’t need:

a PhD
5 more years of experience
years of formal math training

What you need are the right books, written by people who understand how learning actually happens.

Books that:

respect your time
explain ideas before formalism
connect math directly to algorithms

That’s what this list is about.

Below are 10 free, high-quality books that quietly do what most courses fail to do:
they help you understand machine learning, not just use it.

1.Mathematics for Machine Learning, A. Aldo Faisal, and Cheng Soon Ong

https://mml-book.github.io/book/mml-book.pdf

The gold standard for ML math: linear algebra, calculus, probability, clearly connected to algorithms.

2.Dive into Deep Learning, Cambridge University Press

https://d2l.ai

A modern deep learning textbook with math, code, and intuition side-by-side.

3.Think Bayes by Allen B. Downey

https://allendowney.github.io/ThinkBayes2/

Bayesian reasoning explained through code and real examples.

**
4.Think Stats** by Allen B. Downey

https://greenteapress.com/thinkstats2/thinkstats2.pdf

Statistics for people who want to understand data, not memorize formulas.

5.Machine Learning from Scratch By Danny Friedman

https://dafriedman97.github.io/mlbook

Classic ML algorithms built step by step, no black boxes.

6.Patterns, Predictions, and Actions, M. Hardt and B Recht

https://mlstory.org/pdf/patterns.pdf

A conceptual ML book focused on generalization, optimization, and causality.

7.Mathematical Introduction to Deep Learning, A. Jentzen, B. Kuckuck, P. von Wurstemberger

https://arxiv.org/pdf/2310.20360

Neural networks explained from first principles.

**
8.Calculus, by Gilbert Strang, MIT Press**

https://ocw.mit.edu/courses/res-18-001-calculus-fall-2023/mitres_18_001_f17_full_book.pdf

A book that will give you step by step foundation of Calculus.

**
9.Linear Algebra for Machine Learning**, by University of Pennsylvania.
https://www.cis.upenn.edu/~cis5150/linalg-I-f.pdf

The language of data, vectors, and transformations, made practical.

10.Mathematical Theory of Deep Learning

https://arxiv.org/pdf/2407.18384

For readers who want to understand how and why deep learning works under the hood.

Understanding accumulates quietly.

And once it’s there, it doesn’t disappear when the tools change.

Frameworks will be replaced.

APIs will evolve.

Terminology will shift.

The underlying ideas will not.

That’s why these books matter.

They are not about keeping up.

They are not trendy.

They are not optimised for clicks.

They are the kind of resources you come back to years later and think,“I finally see it now.”

And in a field that changes as quickly as machine learning, that turns out to be a long-term advantage.

If this was useful, I write about Math and ML weekly at Math Mindset (mathmindset.substack.com). It's free.

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The most enjoyable and useful Mathematical books of all time

The 10 free books I recommend to every engineer learning ML math (and why order matters)