Top PDF How to Think Like a Computer Scientist: Learning with Python

How to Think Like a Computer Scientist: Learning with Python

How to Think Like a Computer Scientist: Learning with Python

x Preface seeing this example are looking at their first program. Some of them are undoubt- edly a little nervous, having heard that computer programming is difficult to learn. The C++ version has always forced me to choose between two unsatisfying op- tions: either to explain #include, void main(), {, and }, and risk confusing or intimidating some of the students right at the start, or to tell them, “Just don’t worry about all of that stuff now; we will talk about it later,” and risk the same thing. The educational objectives at this point in the course are to introduce students to the idea of a programming language and to get them to write their first program, thereby introducing them to the programming environment. The Python program has exactly what is needed to do these things, and nothing more. Comparing the explanatory text of the program in each version of the book fur- ther illustrates what this means to the beginning student. There are thirteen paragraphs of explanation of “Hello, world!” in the C++ version; in the Python version, there are only two. More importantly, the missing eleven paragraphs do not deal with the “big ideas” in computer programming but with the minutia of C++ syntax. I found this same thing happening throughout the book. Whole paragraphs simply disappear from the Python version of the text because Python’s much clearer syntax renders them unnecessary.
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How to Think Like a Computer Scientist: Learning with Python 3 Documentation - How to Think Like a Computer Scientist - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

How to Think Like a Computer Scientist: Learning with Python 3 Documentation - How to Think Like a Computer Scientist - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

Here are some suggestions for reading programs (and other formal languages). First, remember that formal languages are much more dense than natural languages, so it takes longer to read them. Also, the structure is very important, so it is usually not a good idea to read from top to bottom, left to right. Instead, learn to parse the program in your head, identifying the tokens and interpreting the structure. Finally, the details matter. Little things like spelling errors and bad punctuation, which you can get away with in natural languages, can make a big difference in a formal language.
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Python for Software Design   How to Think Like a Computer Scientist pdf

Python for Software Design How to Think Like a Computer Scientist pdf

Debugging is also like an experimental science. Once you have an idea about what is going wrong, you modify your program and try again. If your hypothesis was correct, then you can predict the result of the modification, and you take a step closer to a working program. If your hypothesis was wrong, you have to come up with a new one. As Sherlock Holmes pointed out, “When you have eliminated the impossible, whatever remains, however improbable, must be the truth” (A. Conan Doyle, The Sign of Four).

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Think Python: How to Think Like a Computer Scientist

Think Python: How to Think Like a Computer Scientist

40 Chapter 4. Case study: interface design Exercise 4.4. The letters of the alphabet can be constructed from a moderate number of basic ele- ments, like vertical and horizontal lines and a few curves. Design a font that can be drawn with a minimal number of basic elements and then write functions that draw letters of the alphabet. You should write one function for each letter, with names draw_a, draw_b, etc., and put your functions in a file named letters.py. You can download a “turtle typewriter” from http: // thinkpython. com/ code/ typewriter. py to help you test your code.

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How to Think Like a Computer Scientist: C Version

How to Think Like a Computer Scientist: C Version

The goal of this book, and this class, is to teach you to think like a computer scientist. I like the way computer scientists think because they combine some of the best features of Mathematics, Engineering, and Natural Science. Like math- ematicians, computer scientists use formal languages to denote ideas (specifi- cally computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions. The single most important skill for a computer scientist is problem-solving. By that I mean the ability to formulate problems, think creatively about solu- tions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That’s why this chapter is called “The way of the program.”
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How to Think Like a Computer Scientist. Java Version

How to Think Like a Computer Scientist. Java Version

The goal of this book, and this class, is to teach you to think like a computer scientist. I like the way computer scientists think because they combine some of the best features of Mathematics, Engineering, and Natural Science. Like math- ematicians, computer scientists use formal languages to denote ideas (specifi- cally computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions. The single most important skill for a computer scientist is problem-solving. By that I mean the ability to formulate problems, think creatively about solu- tions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That’s why this chapter is called “The way of the program.”
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Think Java: How To Think Like a Computer Scientist

Think Java: How To Think Like a Computer Scientist

Identifying logic errors can be hard because you have to work backwards, looking at the output of the program, trying to figure out why it is doing the wrong thing, and how to make it do the right thing. Usually the compiler and the interpreter can’t help you, since they don’t know what the right thing is. Now that you know about the three kinds of errors, you might want to read Appendix C, where we’ve collected some of our favorite debugging advice. It refers to language features we haven’t talked about yet, so you might want to re-read it from time to time.

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How to Think Like a Computer Scientist: C++ Version

How to Think Like a Computer Scientist: C++ Version

One possibility is to model the way humans shuffle, which is usually by dividing the deck in two and then reassembling the deck by choosing alternately from each deck. Since humans usually don’t shuffle perfectly, after about 7 iterations the order of the deck is pretty well randomized. But a computer program would have the annoying property of doing a perfect shuffle every time, which is not really very random. In fact, after 8 perfect shuffles, you would find the deck back in the same order you started in. For a discussion of that claim, see http://www.wiskit.com/marilyn/craig.html or do a web search with the keywords “perfect shuffle.”
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The Phenomenology of Cognition Or What Is It Like to Think That P?

The Phenomenology of Cognition Or What Is It Like to Think That P?

(that tharthritis is a disease), I must make an inference from known facts about how content is determined and about my natural (social) environment. That is, I can identify a thought only by consulting background knowledge and (together with externalist principles of content determination) drawing a conclusion from it. Moreover, since knowledge of the background environmental (social) facts that determine thought contents is not obtainable introspectively, neither is knowledge of those contents themselves. One must consult external sources for information about the properties that determine the contents of one’s thoughts. Further, this is consistent with allowing that one’s knowledge that a conscious thought is occurring is both introspective and non-inferential: externalism only shows that knowledge that a thought is the thought that p requires observation and inference. Hence, contrary to (K1), the identification of a thought is never Immediate, but is always a complex process involving observation and inference as well as introspection and direct apprehension.
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Megaman: Scalable Manifold Learning in Python

Megaman: Scalable Manifold Learning in Python

Manifold Learning (ML) is a class of algorithms seeking a low-dimensional non-linear rep- resentation of high-dimensional data. Thus, ML algorithms are most applicable to high- dimensional data and require large sample sizes to accurately estimate the manifold. De- spite this, most existing manifold learning implementations are not particularly scalable. Here we present a Python package that implements a variety of manifold learning algo- rithms in a modular and scalable fashion, using fast approximate neighbors searches and fast sparse eigendecompositions. The package incorporates theoretical advances in mani- fold learning, such as the unbiased Laplacian estimator introduced by Coifman and Lafon (2006) and the estimation of the embedding distortion by the Riemannian metric method introduced by Perrault-Joncas and Meila (2013). In benchmarks, even on a single-core desktop computer, our code embeds millions of data points in minutes, and takes just 200 minutes to embed the main sample of galaxy spectra from the Sloan Digital Sky Survey— consisting of 0.6 million samples in 3750-dimensions—a task which has not previously been possible.
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pystruct - Learning Structured Prediction in Python

pystruct - Learning Structured Prediction in Python

PyStruct aims at providing a general purpose implementation of standard structured prediction methods, both for practitioners and as a baseline for researchers. It is written in Python and adapts paradigms and types from the scientific Python community for seamless integration with other projects.

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Python   How to Program, 1e pdf

Python How to Program, 1e pdf

techniques presented in Chapter 4 are essential for creating properly structured programs— especially the larger programs and software that system programmers and application pro- grammers are likely to develop in real-world applications. The “divide and conquer” strat- egy is presented as an effective means for solving complex problems by dividing them into simpler interacting components. We begin by introducing modules as containers for groups of useful functions. We introduce module math and discuss the many mathematics-related functions the module contains. Students enjoy the treatment of random numbers and simu- lation, and they are entertained by a study of the dice game, craps, which makes elegant use of control structures. The chapter illustrates how to solve a Fibonacci and factorial problem using a programming technique called recursion in which a function calls itself. Scope rules are discussed in the context of an example that examines local and global variables. The chapter also discusses the various ways a program can import a module and its ele- ments and how the import statement affects the program’s namespace. Python functions can specify default arguments and keyword arguments. We discuss both ways of passing information to functions and illustrate some common programming errors in an interactive session. The exercises present traditional mathematics and computer-science problems, in- cluding how to solve the famous Towers of Hanoi problem using recursion. Another exer- cise asks the reader to display the prime numbers from 2–100.
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Mixing Python and Java How Python and Java can communicate and work together

Mixing Python and Java How Python and Java can communicate and work together

Java Virtual Machine Servlet Container Java Application Code (Server) Java Application Code (Server) Python Interpreter Generated Python Code (Stub) Generated Python Code (Stub) Python[r]

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How Would Parents Think about "Children's Learning in Advance and Winning in the Beginning"

How Would Parents Think about "Children's Learning in Advance and Winning in the Beginning"

With the coming of postmodernism, humanity and reason optimism brings different parenting concept and parenting role. It focuses on children’s future potential and development (Huang, 2004) [13]. Parents’ anxiety comes from the worry about children’s learning (Honore, 2013)[6]. A lot of parents in Taiwan are so afraid of the fact that their children learn too little and too late. This attitude and recognition is very true in Chinese society where parents expect their children to be the best person in the future (Chao, 2011) [3]. It also pushes early childhood education in higher status, and parents need to pay much attention in children’s educating and parenting manner. However, what children express outward are not under their parents’ expectation eventually (Ching, 1991)[14].
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Machine learning with Python -Pandas

Machine learning with Python -Pandas

Nakon što smo naučili osnove korištenja Jupyter Notebook-a, prešli smo na osnove tri Python biblioteke koje će nam pomoći u razumijevanju samog Pandas-a, a to su NumPy, Matplotlib, Scikit-Learn. Proučili smo kako raditi s višedimenzionalnim vektorima, kako crtati različite vrste grafove te kako modelirati skupove podataka.

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Scikit-learn: Machine Learning in Python

Scikit-learn: Machine Learning in Python

The Python programming language is establishing itself as one of the most popular languages for scientific computing. Thanks to its high-level interactive nature and its maturing ecosystem of sci- entific libraries, it is an appealing choice for algorithmic development and exploratory data analysis (Dubois, 2007; Milmann and Avaizis, 2011). Yet, as a general-purpose language, it is increasingly used not only in academic settings but also in industry.

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How to Think about the Problem of Free Will

How to Think about the Problem of Free Will

I have used Professor Baker as an example of a philosopher who has misunderstood what libertarians believe and want because she provides a recent and very clear example of such a philosopher. But heaven forbid that I should be thought to have implied that she is unique or even unusual in this respect. I could cite similar mistakes on the part of many others. Consider Dennett, a philosopher always worth considering. The same sort of mistake is on display in his chapter of Brainstorms, ‘‘On Giving Libertarians What They Say They Want’’ (and in the essay whose title it is 13 ). I repeat: What libertarians want is identical with what soft determinists want: free will, the ability to do otherwise (There is something we libertarians want that soft determinists do not want. We libertarians want to know what is wrong with the well-known arguments for the incompatibility of free will and indeterminism. Soft determinists do not want this, or most of them do not, because they, or most of them, do not think that there is anything wrong with those arguments. But this is not the thing that libertarians say they want that Dennett was talking about). We may consider also in this connection the subtitle of Dennett’s Elbow Room, ‘‘The Varieties of Free Will Worth Wanting.’’ There is only one variety of free will worth wanting because there is only one variety of free will: the ability to do otherwise. And everyone wants that, both those who think human beings have it (libertarians and soft determinists and compatibilists who are not determinists) and most of those who think they do not (most hard determinists).
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Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

Ease of use and quality assurance. Ensemble learning with pycobra is as simple as loading trained scikit-learn machines – or any machine which has a predict method. Visualising involves little to no parameters, and after loading machines it is straightforward to analyse their performance on a particular dataset. In order to ensure code quality, a set of unit tests is provided for all classes in pycobra, and continuous integration via [Travis CI] ensures all commits are tested. The package follows the PEP8 convention, keeping the code easy to read and contribute to.

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it s not what people think of you, it s what you d like them to think.

it s not what people think of you, it s what you d like them to think.

“What I like most about Urban River is not just their ability to come up with great ideas and quality design concepts, but the way that they work with you rather than for you. It's a real skill to be creative but even more so to capture another person’s vision.”

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The scientist, the politician, the artist and the citizen: how water united them

The scientist, the politician, the artist and the citizen: how water united them

Efforts to directly link art, education, and water science are becoming increasingly popular. An example, directly sponsored by a city, is the New York City Department of Environmental Protection’s Annual Water Resources Art and Poetry Contest which is open to 2nd to 12th grade students attending either public, independent, charter or parochial schools or tutored at home. The participants are invited to create original art and compose poetry that reflects an appreciation for the region’s shared water resources, and student participants are annually hon- oured at a city-wide event. The contest is closely aligned with both New York State Learning Standards and New York City Performance Standards for elementary, middle and high school students in the so-called STEM (science, technology, engineering, and maths) fields, as well as the Common Core State Standards for English Language, Arts & Literacy in History/Social Studies, Science and Technical Subjects.
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