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

Think Python: How to Think Like a Computer Scientist

Think Python: How to Think Like a Computer Scientist

In January 1999 I was preparing to teach an introductory programming class in Java. I had taught it three times and I was getting frustrated. The failure rate in the class was too high and, even for students who succeeded, the overall level of achievement was too low. One of the problems I saw was the books. They were too big, with too much unnecessary detail about Java, and not enough high-level guidance about how to program. And they all suffered from the trap door effect: they would start out easy, proceed gradually, and then somewhere around Chapter 5 the bottom would fall out. The students would get too much new material, too fast, and I would spend the rest of the semester picking up the pieces. Two weeks before the first day of classes, I decided to write my own book. My goals were:
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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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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: C++ Version

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

The goal of this book is to teach you to think like a computer scientist. I like the way computer scientists think because they combine some of the best fea- tures of Mathematics, Engineering, and Natural Science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computa- tions). 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.
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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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Think Java: How To Think Like a Computer Scientist

Think Java: How To Think Like a Computer Scientist

When computers appeared on the scene, one of the initial reactions was: “This is great! We can use a computer to generate the tables, so there will be no errors.” That turned out to be true (mostly), but shortsighted. Not much later, computers were so pervasive that printed tables became obsolete. Even so, for some operations, computers use tables of values to get an approx- imate answer, and then perform computations to improve the approximation. In some cases, there have been errors in the underlying tables, most famously in the table the original Intel Pentium used to perform floating-point division (see https://en.wikipedia.org/wiki/Pentium_FDIV_bug).
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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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Python for Software Design   How to Think Like a Computer Scientist pdf

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

Proving that claim is a nontrivial exercise first accomplished by Alan Turing, one of the first computer scientists (some would argue that he was a mathematician, but a lot of early computer scientists started as mathematicians). Accordingly, it is known as the Turing Thesis. For a more complete (and accurate) discussion of the Turing The- sis, I recommend Michael Sipser’s book Introduction to the Theory of Computation. To give you an idea of what you can do with the tools you have learned so far, we’ll evaluate a few recursively defined mathematical functions. A recursive definition is similar to a circular definition, in the sense that the definition contains a reference to the thing being defined. A truly circular definition is not very useful:
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Meaning and uselessness: how to think about derogatory words

Meaning and uselessness: how to think about derogatory words

identifying a pragmatic ingredient to be added to a semantic one given by the word’s neutral counterpart, because only the word itself provides the outlook from which one can make sense of the variety of associated speech acts. The contours of the space of possible acts done with sentences containing particular derogatory words are discernible only from the standpoint of someone who can know what the words mean. Any derogatory word, like any other, has its potential for making speech acts because of what it means. And we are stuck for a way to give the non-descriptive meaning of a derogatory word. (The predicate ‘is D if N’ would do very nicely. But we cannot use this where D is useless for us.)
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Reality and Representations: How Americans Think About Agriculture

Reality and Representations: How Americans Think About Agriculture

The advances in social representation theory made by Moscovici and associates are remarkable. However, the style in which they present their arguments is discursive, and the pattern theory nature makes hypothesis testing difficult. Hence, in order to identify hypotheses it is necessary to first isolate the central propositions using more formal conventions. Prior to outlining these formal propositions, I offer a visual map— Figure 3-1— as a way to articulate more explicitly how information flows in social representation theory. This figure is only meant to be illustrative, and is therefore a parsimonious way to show the process involved with one social representation. There are three levels associated with the individual, group, and supra-group, and corresponding to these are attitudes, paradigms, and social representations, respectively. Attitudes refer to the disposition that individuals have towards some object, person, or idea. Paradigms refer to a fundamental model for understanding reality (Babbie 1986; Kuhn 1996[1962]). Social representations are similar to paradigms in terms of being models of reality, but can differ in that they exist across different groups over a period of time.
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One-Year-Olds Think Creatively, Just Like Their Parents

One-Year-Olds Think Creatively, Just Like Their Parents

turn the box around using the turntable. E spoke while showing the parts of the box to make the interaction more naturalistic. However, the verbal script was not necessary for children to look at the different features of the box. Then E handed an object to the child and a ninety second trial period commenced where the child played freely with the object and box. After 90 seconds, E stopped the child playing, praised them, removed the object and gave them a new object. This was repeated for all 5 objects. If the child asked what the object was or how to use it, E used standardized responses, such as, “ Just play a little while longer. ” Otherwise
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MoL 2015 14: 
  How Difficult is it to Think that you Think that I Think that   ? A DEL based Computational level Model of Theory of Mind and its Complexity

MoL 2015 14: How Difficult is it to Think that you Think that I Think that ? A DEL based Computational level Model of Theory of Mind and its Complexity

Lastly, there is the interesting phenomenon that people seem to have more difficulty with higher-order ToM compared to first-order ToM. Sentences like “I think that you belief in unicorns” are called first-order belief attributions, whereas statements like “I think that you think that I believe in unicorns” are called second-order belief attributions. Second-order belief attribution already seems to be more difficult than first-order belief attribution. For instance, children pass the first-order false-belief task around four years of age (Wellman et al., 2001), while success on second-order versions of this task emerges at about age five or six (Miller, 2009). Both adults and children have been found to make more mistakes on a turn-taking game when it involves second- order reasoning than when it involves first-order reasoning (Flobbe et al., 2008; Hedden & Zhang, 2002). Furthermore, several studies that investigated perspective taking at even higher levels found a prominent drop in performance from the fourth level (Kinderman et al., 1998; Lyons et al., 2010; Stiller & Dunbar, 2007; but see also O’Grady et al., 2015). A commonly held (but debated) view is that higher-order ToM (i.e., beyond first or second level) is cognitively more demanding (see, e.g., Miller, 2009; O’Grady et al., 2015). Therefore, the question arises how the order of ToM contributes to the computational complexity of ToM. This is one of the questions that we investigate in this thesis.
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A Place to Think

A Place to Think

Over a year has passed since the formal end to an autobiographical doctoral journey that led me deep within myself and then outwards again. Following convocation, an emotion-filled moment officially underscoring a perseverance I was not sure I would have, a tenacity that had nearly deserted me on a number of occasions, and the achievement of a title I had not sought for itself but could now proudly display, I quickly found myself in a period of mourning. Throughout the four years of this doctoral journey, all the while maintaining my full-time work as an in-school administrator and then a board-level director, I had often deplored the hectic schedule and the multiple obligations pulling at me from all sides. But the truth was that the academic adventure I had imposed on myself, and in particular the methodology I had chosen for my research, had carved out for me a necessary place to think, a vital place to be. Within this place, I was prompted to reflect not only on the what, the how or even the why which were previously my main considerations as an educator. The heuristic inquiry I conducted was focused on the who, with particular attention to my who.
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Using Think Alouds, Think Afters, and Think Togethers to Research Adolescents’ Inquiry Experiences

Using Think Alouds, Think Afters, and Think Togethers to Research Adolescents’ Inquiry Experiences

The Think Togethers allowed the four participants to spend time talking about the information search process during various stages of the inquiry. Most of their discussion focused on the feelings that were being experienced during the current stage of inquiry, that is, excitement, frustration, relief, anxi- ety, information overload, and so on. During each Think Together session, I encouraged students to look at Kuhlthau’s Information Search Process model (1993) to locate their current feelings and to talk about their progress on their inquiry in relation to the six stages of the model. Participants reported that being able to discuss being through one stage and onto the next was a helpful activity for them. Time to talk about the process and to relate their feelings to a model was also encouraged by the teacher-librarian during instructional times. The teacher-librarian encouraged an open and safe atmosphere and provided opportunities in large-group discussions for students to reflect on the process throughout an inquiry. The teacher-librarian’s attitudes and practices in rela- tion to inquiry-based learning, as evidenced through her work with the class as a whole, contributed to the success of the Think Togethers and Think Afters used as research tools with the four participants. In this study, my research methods were similar to and consistent with strategies that were already in the repertoires of the four participants’ everyday school life.
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At variance with reality: how to re-think our thinking

At variance with reality: how to re-think our thinking

At the time of writing, Britain is being subject to ‘the worst storms for 200 years’, an extraordinary battering with virtually endless rainfall, and widespread flooding and travel disruption, including the severing of the rail link to the South West where I live. It echoes extreme weather events in other parts of the world. Here, amidst the extensive news coverage of lakes and rivers (where there should be fields and roads) and of people flooded out their homes, there is only the most marginal acceptance by media and politicians that it ‘might be’ related to climate change. Rather, there is a sense of shock and anger: how could this happen?
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If you think PBL is right for your students, think again

If you think PBL is right for your students, think again

Problem based learning (PBL) has been widely practised and extensively researched in a variety of disciplines for over past four decades. However, its evidence of effectiveness still remains inconclusive. This literature review paper debates the effectiveness of problem-based learning on learners’ academic performance. Specifically, the discussion in this paper puts the focus on the basic concept of PBL and the effectiveness of PBL based on the existing research findings. Apart from that, cognitive load theory will be discussed as it is generally believed that cognitive load induced by any learning strategy has an impact on learner’s performance. The effectiveness of PBL from the perspective of Cognitive Load Theory will be presented. In addition, this paper also puts forward some theoretical ideas on when to ultilise PBL during the process of teaching and learning, and how the implementation of PBL can be improved by integrating with other learning strategies.
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What I think when I think about treebanks

What I think when I think about treebanks

Nevertheless, explanations based on linguistic grounds are far more common, and we often hear claims based on treebank results that some languages are harder to parse than others. This was also the motivation behind a recent shared task in parsing morphologically rich languages.2 You may think that linguistic differences are much more important than other factors. This is not always true, however. Foster et al. (2010), for example, evaluate a dependency parser trained on newswire (the English Penn Treebank) on hand-annotated Twitter data. On held-out newswire data, the parser has an unlabeled attachment score of 90.6%, but on Twitter data, the score is 73.6%. This 19% relative drop (17% absolute) is bigger than a lot of the drops we observe transferring models across languages. About the same time, Foster et al. (2010) ran the Twitter experiments, McDonald et al. (2011) revisited the idea of transferring the non-lexical part of parsing models across languages. Their impoverished English model scored 82.5% in unlabeled attachment score on English data. When evaluating their model on Portuguese, for example, scores dropped to 68.4%, which is a 17% relative drop (14% absolute). Changing the domain hurts model performance more than changing the language in this case. It is easy to find similar examples in the literature. These factors, by the same token, also influence how well we can generalize from validation and test set performance to performance in the wild or practical usefulness. This paper discusses different dimensions of this problem and proposes design principles for building treebanks in the future. My main observation is that our treebanks are being too nice on us, i.e., leading us to overestimate our performance in the wild.
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To Think or Not to Think?: A New Perspective on Optimal Consumer Decision Making

To Think or Not to Think?: A New Perspective on Optimal Consumer Decision Making

In routine decision making, greater conscious deliberation improved decision quality. Conscious thinkers who spent between 20 and 25 additional seconds thinking (relative to intuitive/control thinkers in studies 1 and 4) improved their decision making in a routine choice. However, conscious deliberation is a limited resource (Simon 1982) and individuals must consider tradeoffs when deciding how long to explicitly deliberate (Bettman, Luce, and Payne 2008). Furthermore, too much conscious deliberation in routine decision making has been identified as problematic (Sela and Berger 2012). Therefore, future research should consider the optimal balance between the benefits and costs of explicit deliberation in routine settings. Several factors could be considered, including the desire to satisfice or optimize, the degree of satisfaction with a previous purchase, and changes in the available brands in subsequent purchase decisions (e.g. straight re-buy vs. modified re-buy; Doyle, Woodside, and Michell 1979).
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If You Think 9 Ending Prices Are Low, Think Again

If You Think 9 Ending Prices Are Low, Think Again

Cross-Correlograms of the Percentage Differences between Average 9-Ending and Non 9Ending Prices, Regular and Sale Prices, on a Weekly Basis, by Product Categories at the Product-Store L[r]

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If You Think 9 Ending Prices Are Low, Think Again

If You Think 9 Ending Prices Are Low, Think Again

In the regression in column 1, which includes dummies for weeks and for subcategories-store, we find that in only five product categories Bath Soaps, Cookies, Frozen Dinners, Frozen Entr[r]

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