#271 in Computers & technology books
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Reddit mentions of Concepts, Techniques, and Models of Computer Programming (The MIT Press)
Sentiment score: 9
Reddit mentions: 15
We found 15 Reddit mentions of Concepts, Techniques, and Models of Computer Programming (The MIT Press). Here are the top ones.
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- Used Book in Good Condition
Features:
Specs:
| Color | Multicolor |
| Height | 1.97 Inches |
| Length | 10.18 Inches |
| Number of items | 1 |
| Release date | February 2004 |
| Weight | 4.40042674952 Pounds |
| Width | 7.9 Inches |
There is a book that explores the programming language paradigms pretty well: CTM.
The book, as expected, is a massive tome at 930 pages.
SICP? Sort of... Otherwise it's mostly papers at readscheme.org (R.I.P.)
EDIT: an interesting non-Lisp book that could yield ideas portable to certain Lisps is Concepts, Techniques, and Models of Computer Programming.
My favorite tome:
Concepts, Techniques, and Models of Computer Programming is 930 pages and worth every page, IMO. A lot of it can be read without the other parts too making it more like 5 books in one.
This looks like a very early draft of this book;
http://www.info.ucl.ac.be/~pvr/book.html
http://www.amazon.com/Concepts-Techniques-Models-Computer-Programming/dp/0262220695
This looks like such a nice book, that I will buy it.
http://www.amazon.com/Concepts-Techniques-Models-Computer-Programming/dp/0262220695/
van Roy, CTM of comp programming
http://www.info.ucl.ac.be/~pvr/VanRoyChapter.pdf
Data Structures & Algorithms is usually the second course after Programming 101. Here is a progression (with the books I'd use) I would recommend to get started:
Edit: If you're feeling adventurous then after those you should look at
This then this.
from the same author
This is an incomplete list of arguments in favor of immutability:
[...]
their usage is side-effect free (no defensive copies)
[...]
and
How can an immutable object represent a mutable entity? Look at an immutable class, File, and its methods, for example length() and delete().
well yeah, an immutable object can represent a mutable entity through side effect, great!
He seems to have built a whole framework of thinking through idiosyncrasies and he gets to conflate in ugly ways the benefits of immutability on values and referential transparency on functions (well methods because java model of oop).
For instance he claims that
truly immutable objects are always thread-safe
This means that multiple threads can access the same object at the same time, without clashing with another thread.
Which is true if you consider objects as bag of values with side-effect free referentially transparent methods, however he calls these "constant" objects as a subgroup of immutable objects. And no, once you have side-effectful methods in the mix, thread-safety gets the boot.
I have to say that the ill defined concept of OOP is prone to this.
I would recommend him this excellent no-nonsense book:
http://www.amazon.com/Concepts-Techniques-Models-Computer-Programming/dp/0262220695
Peter Van Roy co-authored Concepts, Techniques, and Models of Computer Programming
If you can spare the money + time 'Concepts, Techniques and Models of Computer Programming' will give you many paradigms, from functional, concurrent logical, constraint - all within one language. I've never written anything in Oz (its language), but I've enjoyed reading the book.
Concepts, Techniques, and Models of Computer Programming. If the language doesn't matter you get an overview of most language concepts and more importantly concurrent programming concepts. More or less every design I ever saw were using mutable shared state, loaded with zillions of fain grained locks.
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> # Teach Yourself Computer Science
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> If you’re a self-taught engineer or bootcamp grad, you owe it to yourself to learn computer science. Thankfully, you can give yourself a world-class CS education without investing years and a small fortune in a degree program 💸.
>
> There are plenty of resources out there, but some are better than others. You don’t need yet another “200+ Free Online Courses” listicle. You need answers to these questions:
>
> - Which subjects should you learn, and why?
> - What is the best book or video lecture series for each subject?
>
> This guide is our attempt to definitively answer these questions.
>
> ## TL;DR:
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> Study all nine subjects below, in roughly the presented order, using either the suggested textbook or video lecture series, but ideally both. Aim for 100-200 hours of study of each topic, then revist favorites throughout your career 🚀.
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> Subject Why study? Best book Best videos Programming Don’t be the person who “never quite understood” something like recursion. Structure and Interpretation of Computer Programs Brian Harvey’s Berkeley CS 61A Computer Architecture If you don’t have a solid mental model of how a computer actually works, all of your higher-level abstractions will be brittle. Computer Organization and Design Berkeley CS 61C Algorithms and Data Structures If you don’t know how to use ubiquitous data structures like stacks, queues, trees, and graphs, you won’t be able to solve hard problems. The Algorithm Design Manual Steven Skiena’s lectures Math for CS CS is basically a runaway branch of applied math, so learning math will give you a competitive advantage. Mathematics for Computer Science Tom Leighton’s MIT 6.042J Operating Systems Most of the code you write is run by an operating system, so you should know how those interact. Operating Systems: Three Easy Pieces Berkeley CS 162 Computer Networking The Internet turned out to be a big deal: understand how it works to unlock its full potential. Computer Networking: A Top-Down Approach Stanford CS 144 Databases Data is at the heart of most significant programs, but few understand how database systems actually work. Readings in Database Systems Joe Hellerstein’s Berkeley CS 186 Languages and Compilers If you understand how languages and compilers actually work, you’ll write better code and learn new languages more easily. Compilers: Principles, Techniques and Tools Alex Aiken’s course on Lagunita Distributed Systems These days, most systems are distributed systems. Distributed Systems, 3rd Edition by Maarten van Steen 🤷
>
> ## Why learn computer science?
>
> There are 2 types of software engineer: those who understand computer science well enough to do challenging, innovative work, and those who just get by because they’re familiar with a few high level tools.
>
> Both call themselves software engineers, and both tend to earn similar salaries in their early careers. But Type 1 engineers grow in to more fullfilling and well-remunerated work over time, whether that’s valuable commercial work or breakthrough open-source projects, technical leadership or high-quality individual contributions.
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> Type 1 engineers find ways to learn computer science in depth, whether through conventional means or by relentlessly learning throughout their careers. Type 2 engineers typically stay at the surface, learning specific tools and technologies rather than their underlying foundations, only picking up new skills when the winds of technical fashion change.
>
> Currently, the number of people entering the industry is rapidly increasing, while the number of CS grads is essentially static. This oversupply of Type 2 engineers is starting to reduce their employment opportunities and keep them out of the industry’s more fulfilling work. Whether you’re striving to become a Type 1 engineer or simply looking for more job security, learning computer science is the only reliable path.
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> ## Subject guides
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> ### Programming
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> Most undergraduate CS programs start with an “introduction” to computer programming. The best versions of these courses cater not just to novices, but also to those who missed beneficial concepts and programming models while first learning to code.
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> Our standard recommendation for this content is the classic Structure and Interpretation of Computer Programs, which is available online for free both as a book, and as a set of MIT video lectures. While those lectures are great, our video suggestion is actually Brian Harvey’s SICP lectures (for the 61A course at Berkeley) instead. These are more refined and better targeted at new students than are the MIT lectures.
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> We recommend working through at least the first three chapters of SICP and doing the exercises. For additional practice, work through a set of small programming problems like those on exercism.
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> For those who find SICP too challenging, we recommend How to Design Programs. For those who find it too easy, we recommend Concepts, Techniques, and Models of Computer Programming.
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> [Structure and Interpretation of Computer Programs](https://teachyourselfcs.com//sicp.jpg)
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> ### Computer Architecture
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> Computer Architecture—sometimes called “computer systems” or “computer organization”—is an important first look at computing below the surface of software. In our experience, it’s the most neglected area among self-taught software engineers.
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> The Elements of Computing Systems, also known as “Nand2Tetris” is an ambitious book attempting to give you a cohesive understanding of how everything in a computer works. Each chapter involves building a small piece of the overall system, from writing elementary logic gates in HDL, through a CPU and assembler, all the way to an application the size of a Tetris game.
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> We recommend reading through the first six chapters of the book and completing the associated projects. This will develop your understanding of the relationship between the architecture of the machine and the software that runs on it.
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> The first half of the book (and all of its projects), are available for free from the Nand2Tetris website. It’s also available as a Coursera course with accompanying videos.
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> In seeking simplicity and cohesiveness, Nand2Tetris trades off depth. In particular, two very important concepts in modern computer architectures are pipelining and memory hierarchy, but both are mostly absent from the text.
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> Once you feel comfortable with the content of Nand2Tetris, our next suggestion is Patterson and Hennesy’s Computer Organization and Design, an excellent and now classic text. Not every section in the book is essential; we suggest following Berkeley’s CS61C course “Great Ideas in Computer Architecture” for specific readings. The lecture notes and labs are available online, and past lectures are on the Internet Archive.
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> ### Algorithms and Data Structures
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> We agree with decades of common wisdom that familiarity with common algorithms and data structures is one of the most empowering aspects of a computer science education. This is also a great place to train one’s general problem-solving abilities, which will pay off in every other area of study.
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> There are hundreds of books available, but our favorite is The Algorithm Design Manual by Steven Skiena. He clearly loves this stuff and can’t wait to help you understand it. This is a refreshing change, in our opinion, from the more commonly recommended Cormen, Leiserson, Rivest & Stein, or Sedgewick books. These last two texts tend to be too proof-heavy for those learning the material primarily to help them solve problems.
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> (continues in next comment)
Concepts, Techniques, and Models of Computer Programming
Very practical and useful to understand the different programming paradigms. If you want to make sense of functional vs object oriented, and more importantly how different code architecture helps you solve different problems, this one really helps. The real surprise is that it does all that using a single language developed to be multi-paradigm - called Oz -, which really opens your eyes (at least did for me) of what programming languages can be and how these paradigms can be interlaced.
Official website
You're missing concurrent programming, alternative paradigms and algorithms.