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Think Python
An anonymous reader writes "In a neverending effort to spread the word about free quality online programming books, here is a Python programming book. 'How to Think Like a Computer Scientist: Learning With Python', by Allen B. Downey, Chris Meyers, and Jeffrey Elkner is a copylefted work available in multiple formats at Green Tea Press: HTML , PDF, LaTeX. Compliments of the online books what's new page."
Thanks to Copylefted Online Books, I now can read the books before I buy.
On my bookshelf, seven of the books were bought after I read their online version.
I live in a third world country where there is no Towers bookstore, nor Borders, nor Barnes - there is NO WAY for you to know how good a book is without first buying the book - the bookstore here do NOT allow you to read the book !
The idea of Copylefted books really help me, and many others who are in the situation of buying books not knowing if the books are good or not.
Thanks again !
Muchas Gracias, Señor Edward Snowden !
I think part of the problem is the name `Computer Science', which gives a wrong impression of what the core of the poodle really is. That's like calling Astronomy `Telescope Science'. I have met so many people who didn't want to study CS at all - they just wanted to learn `installing Linux and setting up a web server'. This has regrettably put universities under pressure to change their curriculum...
Some universities (eg. Edinburgh) have started calling it `Informatics', which is much more appropriate. (In fact in Germany, and probably elsewhere, it was always called `Informatik'.)
Maybe there should be CS *and* Informatics.
Uhm, guess that was offtopic.
Another excellent free book for Python is Dive Into Python by Mark Pilgrim. It is available in HTML, PDF, Word 97, Windows Help, plain text, and XML formats.
This book has plenty of examples and pointers to further reading on each subject. It features good layout, use of colors, and typography which makes for easy reading and comprehension.
http://www.ibiblio.org/obp/thinkCS.php
I was actually quite surprised to find this article on slashdot. You see, I'm the author of the Perl script which converts the LaTeX source to HTML. I hope nobody finds any blatant problems with the online book websites...
This book doesn't strike me as a book on how to think like a Computer Scientist, except insofar as Computer Scientists generally make lousy Software Engineers. There are no descriptions of the advantages of object oriented programming, discussions of theoretical topics, and in general very little encouragement to view programming as a science. Basically, this appears to be just a book on the Python language, written for someone who has never programmed before. That's a fine thing, don't get me wrong. My brief look even makes me think it could be an effective example of such a book. At the very least, I think it's hyped wrong.
However, from a software engineering point of view, I find it damning that the book forgoes any explanation of the practice of, or motivation for, writing maintainable code. I consider that unforgivable in a beginning programming book. You absolutely have to impress on newbies early the importance of documentation, sensible structure, logical variable naming, good class hierarchy, etc.
I consider this especially true for Python, which is an interpreted non-declarative language (making maintainabilty all that more important). Python is, conversely, also especially well designed as a platform where such concepts could be taught. It largely overcomes the occasional weaknesses of its design philosophy by consciously including language features such as built in support for docstrings, well crafted namespaces, modules as first-class citizens, etc.
Yet, these language features are barely given a nod in this book.
It's books for existing programmers that can afford to skimp on these areas.
Hmm... to be honest... the "Feynman Lectures on Computation" are just about as absurd as the "Goedel Lectures on Biochemistry" (these don't really exist... I'm just being sarcastic). The original poster's comments on Feynman had some merit.
The phrase that you quote here displays a mind-boggling ignorance about exactly what "Computer Science" is. Software Engineering is, indeed, "like Engineering" but there are many branches of Computer Science that deal *purely* with the abstract. I do Formal Language Theory and Automata Theory for a living and I just can't see how these fields are about "getting something to do something". Feynman, like most people, has missed the science for the telescope.
The real joke is that things like the Church-Turing thesis could not possibly be MORE about "natural objects". In the abstract, I can define a machine that can solve the halting problem. Heck, I can define a machine that solves any problem I want! The Church-Turing thesis tells us about a PHYSICAL limitation on computing. In this universe, you can only build a machine that will compute *these* functions.... But what if I live in a universe where time has no meaning? All of a sudden, I get a *very* different Church-Turing thesis.
There is no question that Feynman had some brilliant insights in physics, but I have to admit that when I read the Lectures on Computation, not only did I lose a small amount of respect for him... I found myself actually outraged. Many intelligent people will read these lectures and believe them... I mean, after all, they're written by Feynman, right?
Computer Scientist's have enough trouble trying to explain to people that, no, we don't just sit around installing Windows network drivers all day without a respected and intelligent person like Feynman adding to the problem.
I've written a review of this book on The Assayer. The book is self-published (the authors run Green Tea Press), and one of the things people don't realize about self-publishing is how hard it is to attract reviews. (Actually, it's hard in ordinary publishing, and even harder in self-publishing.) Without reviews, you don't get much credibility. So if there's a free book in The Assayer's database that you've read, please write a review!
Find free books.
I think it's rather silly to teach Computer Science with a specific programming language at all. Computer Science is not about teaching people how to program.
To me, Computer Science is a very cool blend of:
- Discrete mathematics
- Computation theory
- Linguistics
- Complexity theory
- Logic
- Probability
None of these rely on programming at all, let alone a specific language or whether that language has pointers or not. Programming is only an application of Computer Science.
You gave example of an AI class- to me, the core of AI is learning about first-order logic and predicate calculus, searching and graph traversal heuristics, reasoning, and natural language processing. I know this list isn't complete, but the point is that these concepts are for the most part independent of programming. Sure, you can start off by teaching somebody a specific implementation in Lisp or Prolog, but then they only know program AI in Lisp instead of how to apply AI concepts in any setting.
"The defense of freedom requires the advance of freedom" - George W Bush
You have failed to understand the point of Computer Science (pun intended). Python is a terrific language for teaching CS because it has the basics of discrete structures: lists, maps (in Python, called dictionaries), tuples, and atomic data types such as strings, ints, and reals. That's all you need.
There's really nothing you can't do once you have lists and maps. Don't object that you can't have O(1) access-time arrays -- you can do that with a map.
I challenge you to describe any algorithm at all that can't be implemented without pointers. If you think you need pointers, you just aren't thinking like a computer scientist.
For some classes, such as AI, there's simply no winner for Prolog, and perhaps Lisp
In general, you are absolutely correct. Of course, this is opinionated and others may disagree. But remember, you can use any Turing-complete language to simulate any other Turing-complete language (that's the entire definition of Turing-complete). Which means I can write a C interpretter in Prolog if I want (and I'm feeling particularly masochistic), and therefore I can simulate pointers using Prolog.
Oh, but you cry "That can't possibly be efficient!" Right again. But you've again missed the point of Computer Science. CS is about efficient algorithms, not efficient programs. That's something we leave to the software engineers and other "implementors." Us CS freaks think about what can be done, we don't actually do it ;-)
No doubt, Feynman was a very, very good physicist. But he was also a genius at self-promotion, and his cult has gone way overboard as a result. It's well-established by now that some of the ideas he's famous for were first published by others.
(Not that he wasn't honest about it sometimes. I think he's on record, for instance, crediting Stueckelberg for the renormalizion of electrodynamics, and for the idea that positons are electrons travelling backwards in time. See e.g. this timeline, or the last chapter of this book.)
Timeo idiotikOS et dona ferentes
That said, I don't know if I would teach a begining computer science course in python. At my University, our general intro to CSE involves a two class series teaching generic basic theory wrapped around a programming language. We used to teach them with C and C++ but just recently moved to Java. I have been a TA for these classes before. Based on my experiences, I think there are both pluses and minuses to the idea of teaching these classes in python.
Benefits:
- Python is extremely easy to learn, as mentioned before. Much easier than C, C++, or Java.
- Python works really well with Tk, which would make it easy to build out skeleton code (multiplatform skel code at that) for the students using windowing and graphics. Students are 100% happier if they can see what they're working on reflected graphically. Makes it more fun to show off. This is why our projects usually include basic games.
- BASIC Python is truly, completely, multiplatform, working identically on Mac, Win*, and *nix. Some specialized functions in modules don't support all platforms, but nothing that would be important to a begining student. Support issues would be MUCH simpler than C or C++. God, we had huge headaches trying to support MSVC, CodeWarrior, CodeWright, Borland, etc. . .
- There is a great installer script available that will build python modules into either standalone exe's or distributable directories. (Available here if you've never seen this before)
But, there are also some downsides that would have to be weighed. These are:Looking away from basic intro classes, python is great to know. I did a lot of AI code sketches in python, and have used it to slap together simple programs at work. However, I would consider it a tool to be learned after the basics have been beat in. If I had learned python first, it would be a lot harder to force me to do everything in C later.
-s
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Don't worry, being eaten by a crocodile is just like going to sleep in a giant blender.
At low level, pointers are everything, and low level is what you want to teach when you're teaching basic data structures and algorithms.
Conceptually and from a computer science perspective, the object references present in languages like Python, Java etc. are equivalent to pointers in all the ways that matter for representation of data structures and algorithms. In the academic community and elsewhere, it's generally considered beneficial to teach such things without reference to the machine pointers which you're referring to, since machine pointers carry a lot of baggage that's unrelated to the abstractions involved in data structures and algorithms.
There's simply no point in demonstrating list implementation with an interpreted language that has very efficient native lists, dictionaries, etc.
To refute this, let me offer a tutorial: A Gentle Introduction to ML. If you work through this tutorial, you'll very soon begin implementing functions in the ML language for basic list operations and the like - functions that already exist in the language. And guess what: the implementations that the beginner typically comes up with in that tutorial are very close to the actual implementations that ML uses - the tutorial gives some examples of actual implementations for comparison.
This high-level operation doesn't even cost much -languages in the ML family, including OCaml, regularly are top performers - see e.g. Doug Bagley's language shootout. They can perform on par with languages like C because their type systems allow sophisticated compile-time optimizations to be performed, and their high-level abstraction features are supported by optimizations such as tail recursion.
C/C++ or Pascal are much better for that; with them you can teach real implementations, not toy ones.
If you believe that C/C++ and Pascal are good languages for teaching computer science, you don't know much about modern computer science. All three of those languages have very weak type systems and lack basic features that allow the construction of high-level abstractions.
Pascal is all but a dead language in the CS community nowadays. The primary use for C is as a decent portable assembler. Learning C has very little to do with computer science, and absolutely nothing to do with teaching computer science concepts.