Algorithms unlocked
"This book offers an engagingly written guide to the basics of computer algorithms. In Algorithms Unlocked, Thomas Cormen- coauthor of the leading college textbook on the subject- provides a general explanation, with limited mathematics, of how algorithms enable computers to solve problems. Readers will learn what computer algorithms are, how to describe them, and how to evaluate them. They will discover simples ways to search for information in a computer; methods for rearranging information in a computer into a prescribed order ("sorting"); how to solve basic problems that can be modeled in a computer with a mathematical structure called a "graph" (useful for modeling road networks, dependencies among tasks, and financial relationships); how to solve problems that ask questions about strings of characters such as DNA structures; the basic principles behind cryptography; the fundamentals of data compression; and even that there are some problems that no one has figured out how to solve on a computer in a reasonable amount of time." -- Back cover.
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7recommend
10mentions
8people
2013–2021years
90% recommend10% neutral
Mentions per year
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- I liked "Algorithms Unlocked" by Cormen (https://mitpress.mit.edu/books/algorithms-unlocked). It's the same author that co-wrote "Introduction to Algorithms" (or CLRS) that's referenced in a few other comments, but this one focuses on the what and why of algorithms and pulls samples from a few areas of interest.
It's not too long (~200 pages), so might be worth checking out before diving into one of the more comprehensive textbooks.
If you're interested in 3D programming, I found "3D Math Primer for Graphics and Game Development" to be the most accessible for the math fundamentals. I just discovered it's now available freely online as well: https://gamemath.com/.
- I went through my recent-books shelves and found a few shorter books that are well worth reading. Short books for developers are rare these days.
Algorithms Unlocked, Thomas Cormen, 212 pages
The Art of Readable Code, Dustin Boswell & Trevor Foucher, 180 pages
How To Take Smart Notes, Sonke Ahrens, 151 pages
How to Write a Thesis, Umberto Eco, 223 pages
How Charts Lie, Alberto Cairo, 193 pages
- Warning: These books are kind of all over the place as far as technical prerequisites go, but they all satisify being heavier than pop-sci and lighter than a textbook. :)
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Aleksandrov et al - Mathematics: Its content, Methods, ad Meaning. Overview of undergrad curriculum by a number of prominent russian mathematicians.
Levi - The mathematical Mechanic. Uses various physical intuitions and scenarios to solve mathematical problems.
Pask - Magnificent Principia. Tour of newtons principia that includes both newtons geometric language and modern vector calculus version.
Stillwell - Elements of Mathematics. Discusses some important classical problems in the history of mathematics taken from 8 subjects: arithmetic, computation, algebra, geometry, calculus, combinatorics, probability, logic. Wow!
Yandell - The Honors Class: Hilbert's problems and their solvers. Lots of mini biographies with some information describing each of Hilbert's famous problems.
Lawvere & Schanuel- Conceptual Mathematics. Some basic notions of category theory, geared towards teaching you Lawvere's clarification and generalization of the incompleteness theorems.
Cormen - Algorithms Unlocked. Pop Science version of some of the textbook.
Nahin - Inside interesting Integrals. Lots of fun integrations!
Nahin - He also wrote some books about euler and imaginary numbers which ive partially read, they seemed good.
Pierce - An introduction to information theory; symbols signals and noise. sounds and looks like a dover textbook but is a non-fake pop science book.
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Boyer - The History of Calculus and its Conceptual Development. A bit old, and spends too much time on formalism in the 18th and 19th century, there might be something better out now.
Weyl - Philosophy of Mathematics and Natural Science.
Reichenbach - The philosophy of Space and time.
Klein - Elementary mathematics from the advanced viewpoint. these were written at the beginning of the 20th century so are slightly outdated, but kline was extremely forward-looking in his point of view.
Courant & Robbins - What is Mathemaics. Another classic.
Hilbert & Cohn-Vossen - Geometry and the Imagination. The gold standard of popular mathematics writing. A beautiful book.
- As a follow up to this resource I recommend,
Algorithms unlocked: https://www.amazon.com/Algorithms-Unlocked-Press-Thomas-Corm...
CLRS: https://www.amazon.com/Introduction-Algorithms-3rd-MIT-Press...
Both include the same author as the one in this article (Thomas Cormen).
- This is just my opinion and I'm sure it differs from others...
Roughgarden's class is advance and expects mathematical maturity. You may find his course quite fast and rough if you are a beginner.
Sedgwick's class is much easier. He is a bit boring and tries to use "real life" examples (in some instances) from the physical sciences to make the material relatable. This in my opinion detracts from the material. Also, he doesn't always fully explain where he got some of the big ohs here and there.
My advice? Follow MIT's OCW course (it uses CLRS). Supplement it with Algorithms Unlocked, the Khan Academy link in OP and CLRS. If you use those 4 resources and put in the work you'll understand the material.
All 4 sources have Thomas C's DNA touch to it (he is the C in CLRS). So you'll find it consistent when you read from one source to the other. After reading/hearing the same thing about 4 different times in 4 different ways it'll begin to click.
Order of easiness is probably Khan Academy > Algorithms Unlocked > MIT Algorithms Course > CLRS.
Algorithms Unlocked is like "pre-CLRS" and Khan Academy's version is the TL;DR version of Algorithms Unlocked.
Hope this helps.
Below are the links,
https://www.amazon.com/Algorithms-Unlocked-Press-Thomas-Corm...
https://www.amazon.com/Introduction-Algorithms-3rd-MIT-Press...
https://www.khanacademy.org/computing/computer-science/algor...
https://ocw.mit.edu/courses/electrical-engineering-and-compu...
- I recommend learning discrete mathematics, then data structures and algorithms.
I cannot stress enough how important mathematical foundations is. It'll make everything else much easier to learn. I haven't read the book but heard good things about: https://www.amazon.com/Discrete-Mathematics-Applications-Sus... as a beginner text.
Coursera has multiple offerings on Data Structures / Algorithms -- find one that works best for you.
For instance:
https://www.coursera.org/learn/introduction-to-algorithms
https://www.coursera.org/specializations/algorithms
https://www.coursera.org/learn/algorithmic-thinking-1
https://www.coursera.org/learn/algorithmic-toolbox
By doing all of those you'll get a good introductory exposure to the topics.
You should also look at a rigorous course offering of Algorithms. MIT has a few online to view.
Some readings for a beginner are:
https://www.khanacademy.org/computing/computer-science/algor...
https://www.amazon.com/Algorithms-Unlocked-Press-Thomas-Corm...
https://www.amazon.com/Introduction-Algorithms-3rd-MIT-Press... (not beginner level but classic)
After all of this you should be fine with diving into interview books. You'll want to whiteboard solutions and be able to do all the difficult problems. Look into sites like leetcode, glassdoor and be able to do the difficult problems posted there.
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