Simple and intuitive user interface. Nice prints. No warm-up time: starts printing right away.

But I had to replace the print head twice. Basically, a print head lasts a year. Not sure why, as the printer is rarely used. The print head runs at about $35 at Kodak. It looks like Kodak is now selling them as "Out of Warranty". Is anyone surprised?

Also, my printer often takes two sheets at a time from the feeder. Part of the header gets printed on the first sheet, while the rest of the page goes on the second sheet. Waste of time, paper and ink.

Would not buy this printer again. It's not reliable.

But I had to replace the print head twice. Basically, a print head lasts a year. Not sure why, as the printer is rarely used. The print head runs at about $35 at Kodak. It looks like Kodak is now selling them as "Out of Warranty". Is anyone surprised?

Also, my printer often takes two sheets at a time from the feeder. Part of the header gets printed on the first sheet, while the rest of the page goes on the second sheet. Waste of time, paper and ink.

Would not buy this printer again. It's not reliable.

3 of 3 people found the following review helpful

Good paper and color, and a good mix of text and beautiful illustrations. This book is a pleasure to read whether you have a piano/keyboard or not.

It has actually helped me learn the basics of keyboard playing and music reading. It's concise and informative, not a lot of fluff. It tells you when you need more practice to acquire specific skills, and it suggests that you take it slow and move forward only after you master those skills. At the end, you'll find a 24-page colored chord dictionary. Very nice and helpful.

It has actually helped me learn the basics of keyboard playing and music reading. It's concise and informative, not a lot of fluff. It tells you when you need more practice to acquire specific skills, and it suggests that you take it slow and move forward only after you master those skills. At the end, you'll find a 24-page colored chord dictionary. Very nice and helpful.

46 of 49 people found the following review helpful

The book is very good for the intermediate-to-advanced data analysts. Beginners beware: there are some important prerequisites that are not obvious before you buy it, and there are some organization problems.

First, the prerequisites. "I strongly recommend that you make it a habit to avoid all statistical language"..."Once we start talking about standard deviations, the clarity is gone." These are two sentences in the same passage from the Preface. The rest of that passage is similar. However, even the first chapters make heavy use of statistical language. Moreover, they assume that you already know statistics to the level of density estimation, noise, splines, and regression. Page 21 even features a footnote about the Fourier transform and Fourier convolution theorem. Clearly this book is not for the statistically-shy or for mathematically-shy in general, no matter what the Preface suggests. You also need to know Python and R.

Second, the chapter organization problems. There's a mismatch between the first part of each chapter, which introduces concepts and techniques, and the Workshop part of the same chapter, which uses software. I was expecting the Workshop to illustrate the implementation of the same concepts and techniques. It's not really so. The Workshop introduces Python and R facilities at a different (lower) speed than the rest of the chapter. One could even wonder why the Workshop is in the same chapter. I'd rather that each chapter consisted of a few detailed case studies that first introduce concepts and techniques and then illustrate them with software libraries.

First, the prerequisites. "I strongly recommend that you make it a habit to avoid all statistical language"..."Once we start talking about standard deviations, the clarity is gone." These are two sentences in the same passage from the Preface. The rest of that passage is similar. However, even the first chapters make heavy use of statistical language. Moreover, they assume that you already know statistics to the level of density estimation, noise, splines, and regression. Page 21 even features a footnote about the Fourier transform and Fourier convolution theorem. Clearly this book is not for the statistically-shy or for mathematically-shy in general, no matter what the Preface suggests. You also need to know Python and R.

Second, the chapter organization problems. There's a mismatch between the first part of each chapter, which introduces concepts and techniques, and the Workshop part of the same chapter, which uses software. I was expecting the Workshop to illustrate the implementation of the same concepts and techniques. It's not really so. The Workshop introduces Python and R facilities at a different (lower) speed than the rest of the chapter. One could even wonder why the Workshop is in the same chapter. I'd rather that each chapter consisted of a few detailed case studies that first introduce concepts and techniques and then illustrate them with software libraries.

2 of 2 people found the following review helpful

I tried Joola Python and Joola Champ too. They are lighter and faster than Vega.

Vega gives you better control and spin. It seems heavy at first, but you'll get used to it.

Better choice for defensive players.

Vega gives you better control and spin. It seems heavy at first, but you'll get used to it.

Better choice for defensive players.

5 of 6 people found the following review helpful

It successfully charged a dead battery on a German car from another German car (different makes). It did the job in 20 minutes as advertised. No problem.

I don't give it 5 stars because I can't find the cable length typed on the box. Also, the user manual doesn't say whether the engine of the good car should be running or not during the charging operation. In my case it was running.

The manual also says that you need to turn the ignition key on the "on" position to enable the cigarette lighter on some cars, but it doesn't say which cars need that. Mine didn't.

So you might need some trial & error to get this device working with your car. If you can afford that in a cold morning.

I don't give it 5 stars because I can't find the cable length typed on the box. Also, the user manual doesn't say whether the engine of the good car should be running or not during the charging operation. In my case it was running.

The manual also says that you need to turn the ignition key on the "on" position to enable the cigarette lighter on some cars, but it doesn't say which cars need that. Mine didn't.

So you might need some trial & error to get this device working with your car. If you can afford that in a cold morning.

47 of 47 people found the following review helpful

Buy this book only if you:

1. Know the basics of natural language processing (NLP) or linguistics;

2. Know the Python programming language or you're willing to learn it;

3. Are using the NLTK library or plan to do so.

NLTK is a Python library that offers many standard NLP tools (tokenizers, POS taggers, parsers, chunkers and others). It comes with samples of several dozens of text corpora typically used in NLP applications, as well as with interfaces to dictionary-like resources such as WordNet and VerbNet. No FrameNet, though. NLTK is well documented, so you might not need this book initially. However, it definitely helps to have it on your desk if you are serious about using NLTK.

The first chapters are a bit messy, as they attempt to introduce all three themes (NLP, NLTK and Python) together. Beginners may have some difficulty sorting things out. By the time you reach the WordNet section, you either got lost in the forest, realize that you would never understand this topic without the book, or both. However, if you are a bit patient and try out all simple code examples, you'll make it eventually. In my opinion, NLTK remains the simplest, most elegant and well rounded library of its kind.

1. Know the basics of natural language processing (NLP) or linguistics;

2. Know the Python programming language or you're willing to learn it;

3. Are using the NLTK library or plan to do so.

NLTK is a Python library that offers many standard NLP tools (tokenizers, POS taggers, parsers, chunkers and others). It comes with samples of several dozens of text corpora typically used in NLP applications, as well as with interfaces to dictionary-like resources such as WordNet and VerbNet. No FrameNet, though. NLTK is well documented, so you might not need this book initially. However, it definitely helps to have it on your desk if you are serious about using NLTK.

The first chapters are a bit messy, as they attempt to introduce all three themes (NLP, NLTK and Python) together. Beginners may have some difficulty sorting things out. By the time you reach the WordNet section, you either got lost in the forest, realize that you would never understand this topic without the book, or both. However, if you are a bit patient and try out all simple code examples, you'll make it eventually. In my opinion, NLTK remains the simplest, most elegant and well rounded library of its kind.

2 of 2 people found the following review helpful

Very good choice as your first book on basic (discrete) probability.

Lots of classical examples that you will encounter in more advanced classes too. The birthday problem, the occupancy problem, college grades and Simpson's paradox, medical diagnosis and Bayes' theorem, basic cryptology, simple English language models (the Shakespeare monkey) and many others. This book is a very good introduction to them.

It uses the urn model to develop many solutions to problems, which is a good thing because that is a general and powerful model.

Note that the book does not cover continuous distributions nor statistical inference.

Lots of classical examples that you will encounter in more advanced classes too. The birthday problem, the occupancy problem, college grades and Simpson's paradox, medical diagnosis and Bayes' theorem, basic cryptology, simple English language models (the Shakespeare monkey) and many others. This book is a very good introduction to them.

It uses the urn model to develop many solutions to problems, which is a good thing because that is a general and powerful model.

Note that the book does not cover continuous distributions nor statistical inference.

3 of 3 people found the following review helpful

My first fhd2400 was a floor model in perfect shape. After about 3 months it got a vertical red line on the screen, going from top to bottom. Chatted online with Gateway tech support, they advised me to send the monitor to the service center for warranty repairs, which I did. I got it back in about a month (after a couple of calls and some complaining about their slow service).

The monitor has an above-the-average amount of bleeding, more than 1 inch on every edge. But I don't care much about that.

I actually liked the display so much that I bought a second one, in refurbished condition. This one runs great so far. No dead/stuck pixels on any of them.

Overall, a very cool, glossy and not expensive 24" HD LCD with unexpected extras such as picture-in-picture, 4 USB ports, portrait mode and all the A/V inputs you'll ever need (check out the technical specs!). I like the look-and-feel too much to let the problems stand in the way.

The monitor has an above-the-average amount of bleeding, more than 1 inch on every edge. But I don't care much about that.

I actually liked the display so much that I bought a second one, in refurbished condition. This one runs great so far. No dead/stuck pixels on any of them.

Overall, a very cool, glossy and not expensive 24" HD LCD with unexpected extras such as picture-in-picture, 4 USB ports, portrait mode and all the A/V inputs you'll ever need (check out the technical specs!). I like the look-and-feel too much to let the problems stand in the way.

2 of 2 people found the following review helpful

No bells and whistles here. Basic LCD with solid stand that only allows a bit of tilting. A bit heavier than expected, compared to the similar Acer 22" model.

Mine has a stuck red pixel, but I'm used to that. It also produces a high-frequency sound that might be a problem in a perfectly quiet room -- not my case.

The overall quality of the image is OK. I can distinguish white from almost-white and black from almost-black. It failed one of my software tests using graphic patterns that most people will never encounter in practice.

I bought it new for its very good (sale) price and the 3-year warranty. There's not much else to say about this monitor.

Mine has a stuck red pixel, but I'm used to that. It also produces a high-frequency sound that might be a problem in a perfectly quiet room -- not my case.

The overall quality of the image is OK. I can distinguish white from almost-white and black from almost-black. It failed one of my software tests using graphic patterns that most people will never encounter in practice.

I bought it new for its very good (sale) price and the 3-year warranty. There's not much else to say about this monitor.

13 of 22 people found the following review helpful

The preface states that the main purpose of this book is to provide usable implementations of some of the most useful algorithms. To illustrate the superiority of this approach as compared to similar books, the authors present the pseudo-code of the Ford-Fulkerson algorithm as given in Wikipedia and Cormen's textbook. Then they write that such listings are basically useless for a software engineer, who cannot produce working programs from them. He needs a book with real, working, verified implementations rather than pseudo-code and proofs of correctness. This is that book.

After reading this part of the preface in a local bookstore, I read the table of contents and picked at random the section on Linear Programming for a closer look at the contents. This 2-page section provides no code whatsoever. Not even pseudo-code. It uses a commercial mathematical software to solve a problem and advises the readers to do the same. Don't implement anything here because it's too complicated, just use a commercial package. I found this message a bit amusing, and somehow opposed to the stated purpose of the book.

Maybe I was unlucky, and other sections are different. You'll have to check that for yourselves. The book seems otherwise compact in its field, reasonably priced, rich in tables, examples, illustrations and other attention-grabbers. I'd say buy it, but take its promises with a grain of salt.

Incidentally, the Wikipedia article on Ford-Fulkerson provides a Python implementation in addition to the pseudo-code. It might have been added after this book was published. Nevertheless, this shows how quickly some parts of a 2008 book can become outdated.

After reading this part of the preface in a local bookstore, I read the table of contents and picked at random the section on Linear Programming for a closer look at the contents. This 2-page section provides no code whatsoever. Not even pseudo-code. It uses a commercial mathematical software to solve a problem and advises the readers to do the same. Don't implement anything here because it's too complicated, just use a commercial package. I found this message a bit amusing, and somehow opposed to the stated purpose of the book.

Maybe I was unlucky, and other sections are different. You'll have to check that for yourselves. The book seems otherwise compact in its field, reasonably priced, rich in tables, examples, illustrations and other attention-grabbers. I'd say buy it, but take its promises with a grain of salt.

Incidentally, the Wikipedia article on Ford-Fulkerson provides a Python implementation in addition to the pseudo-code. It might have been added after this book was published. Nevertheless, this shows how quickly some parts of a 2008 book can become outdated.

1 of 1 people found the following review helpful

Good, powerful, clear sound with deep bass.

Very light headset, comfy, you might forget you have it on your head.

The almost non-existent pressure on your ears, however, does not help with noise canceling. So you'd better have a quiet room.

Finally, the whole thing seems fragile. Don't drop it.

Very light headset, comfy, you might forget you have it on your head.

The almost non-existent pressure on your ears, however, does not help with noise canceling. So you'd better have a quiet room.

Finally, the whole thing seems fragile. Don't drop it.

You do get a stronger bass than expected from such a small headset. However, in order to actually hear the difference, you need to either turn up the volume a lot or press the ear-cuffs to your ears.

1 of 5 people found the following review helpful

You get your bass thump for a reasonable price.

I like the black-red color combination and the overall design of this unit.

The ear-cuffs are very large. They completely cover the ears and reach well below and behind them.

The package was ridiculously difficult to open. My scissors barely made a dent, then the paper cutter almost broke so I had to use a serious knife. Come on guys, this is a gaming ear set not a bank vault!

I like the black-red color combination and the overall design of this unit.

The ear-cuffs are very large. They completely cover the ears and reach well below and behind them.

The package was ridiculously difficult to open. My scissors barely made a dent, then the paper cutter almost broke so I had to use a serious knife. Come on guys, this is a gaming ear set not a bank vault!

6 of 7 people found the following review helpful

A reasonably sized book that delivers thoughtful explanations, as promised in the title. On average, each chapter is 15-page long, therefore it can be read while enjoying a cup of tea. And then you can go to bed satisfied that you actually finished a chapter today, and finally understood why they call it the power of a test.

ANOVA seems to form the core of this book, in its various guises and usages. But my favorite chapter is the last one, which introduces the general linear model as an umbrella for most statistical concepts presented in the book. This has been a fresh take on the whole subject for me when I first read it.

I take away one star because the book has no exercises for the readers to sharpen their pencils. The chapters contain some worked examples, but these are not enough for students. So you would need another book for that purpose. Also, this book is very light on probability topics.

ANOVA seems to form the core of this book, in its various guises and usages. But my favorite chapter is the last one, which introduces the general linear model as an umbrella for most statistical concepts presented in the book. This has been a fresh take on the whole subject for me when I first read it.

I take away one star because the book has no exercises for the readers to sharpen their pencils. The chapters contain some worked examples, but these are not enough for students. So you would need another book for that purpose. Also, this book is very light on probability topics.

3 of 4 people found the following review helpful

I spent some 20 mins browsing this book at my local bookstore. I believe the author states that a random sample is the same thing as a simple random sample. This is not really so. In a random sample, every unit has the same probability of being chosen as every other unit. In a simple random sample (SRS), the whole sample (n units) has an equal probability of being chosen as every other sample of n units from the same population. A given sample may be a random sample and not necessarily be a simple random sample.

Some other statistics books are also using random sample as a shortcut expression for simple random sample, but beware that they are actually two different things.

Otherwise, this book seemed comprehensive and well organized to me. It teaches a little more statistics than you need for the AP exam, and it does not provide as much AP-sepcific insider hints as Duane Hinders' book.

Some other statistics books are also using random sample as a shortcut expression for simple random sample, but beware that they are actually two different things.

Otherwise, this book seemed comprehensive and well organized to me. It teaches a little more statistics than you need for the AP exam, and it does not provide as much AP-sepcific insider hints as Duane Hinders' book.

16 of 17 people found the following review helpful

You will likely ace your AP Statistics exam if you use this book (I wouldn't know, since I only used it for individual study). There are enough examples to illustrate the concepts. All exercises have solutions, and they are generally carefully chosen. You have a diagnostic test, and explanations on how the grade is calculated. Compared to similar AP Statistics books this one seems more to the point, so I recommend it.

However, some definitions could be more carefully worded and more consistent. Sometimes, the theoretical presentation gives me the impression that the author would describe things rather than define them properly.

For example, on the bottom of page 50 we read "a Random variable can be thought of as a numerical outcome of a random phenomenon or experiment". This passes as a concept description (although, strictly speaking, a random variable is a numerical function, not a numerical outcome; also, this definition may suggest that random variables make sense only in experiments with numerical outomes). However, on top of page 148 we read that "a random variable, X, is a numerical value assigned to an outome of a random phenomenon". So now the random variable is not a numerical outcome anymore, but rather a numerical value associated to an outcome. And it gets worse when using the notation P(X=x) to express that "the random variable X takes on the particular value of x". How can a random variable, which was defined as a value, take on a particular value? A value is a value, like "3", and it cannot take on another value. I think the statistics books should drop the habit of giving such pseudo-definitions to random variables. Tell a spade a spade: a random variable is a function defined on the sample space.

Page 143 features a messy mix between the concepts of "outcomes" and "events". Let's be clear: events are sets of outcomes. They cannot be mixed together. Thus it is wrong to say that "outcomes are sometimes called simple events". The correct definition is this: a simple event is a set that contains exactly one outcome.

On the same page, we read that "the probability of an event is the relative frequency of the outcome". Which outcome is that, anyway, since an event is a set of possible outcomes? This again mixes sets and elements of sets in the same sentence.

The definition of a probability distribution for a Continuous Random Variable given on pp 151 is cyclic, is not actually a definition and it is rather confusing. The fact that it is cyclic is apparent in these two fragments: "There is a smooth curve, called a density curve (defined by a density function)..." and "In this course, there are several CRVs for which we know the probability density functions (a probability distribution defined in terms of some density curve)..." It appears that each of the two concepts (density function and density curve) is defined using the other one.

Finally, by the time I reached the chapter on confidence intervals, I found myself doing what some teachers call "mindless calculations". Perhaps it is a sign that this book gives more recipes than explanations.

I would like to read a new edition of this book, in which the author spells out the definitions more clearly and beefs up the theory a little.

However, some definitions could be more carefully worded and more consistent. Sometimes, the theoretical presentation gives me the impression that the author would describe things rather than define them properly.

For example, on the bottom of page 50 we read "a Random variable can be thought of as a numerical outcome of a random phenomenon or experiment". This passes as a concept description (although, strictly speaking, a random variable is a numerical function, not a numerical outcome; also, this definition may suggest that random variables make sense only in experiments with numerical outomes). However, on top of page 148 we read that "a random variable, X, is a numerical value assigned to an outome of a random phenomenon". So now the random variable is not a numerical outcome anymore, but rather a numerical value associated to an outcome. And it gets worse when using the notation P(X=x) to express that "the random variable X takes on the particular value of x". How can a random variable, which was defined as a value, take on a particular value? A value is a value, like "3", and it cannot take on another value. I think the statistics books should drop the habit of giving such pseudo-definitions to random variables. Tell a spade a spade: a random variable is a function defined on the sample space.

Page 143 features a messy mix between the concepts of "outcomes" and "events". Let's be clear: events are sets of outcomes. They cannot be mixed together. Thus it is wrong to say that "outcomes are sometimes called simple events". The correct definition is this: a simple event is a set that contains exactly one outcome.

On the same page, we read that "the probability of an event is the relative frequency of the outcome". Which outcome is that, anyway, since an event is a set of possible outcomes? This again mixes sets and elements of sets in the same sentence.

The definition of a probability distribution for a Continuous Random Variable given on pp 151 is cyclic, is not actually a definition and it is rather confusing. The fact that it is cyclic is apparent in these two fragments: "There is a smooth curve, called a density curve (defined by a density function)..." and "In this course, there are several CRVs for which we know the probability density functions (a probability distribution defined in terms of some density curve)..." It appears that each of the two concepts (density function and density curve) is defined using the other one.

Finally, by the time I reached the chapter on confidence intervals, I found myself doing what some teachers call "mindless calculations". Perhaps it is a sign that this book gives more recipes than explanations.

I would like to read a new edition of this book, in which the author spells out the definitions more clearly and beefs up the theory a little.

5 of 9 people found the following review helpful

The first one stopped working for no apparent reason after about one year. The second one is way too noisy. This means that the third one will definitely be a different brand. Too bad, as this model is cheap, lightweight and it does the job... as long as it works.

7 of 16 people found the following review helpful

I am now reading the second edition of this book, printed in 1978. It is a low-cost Collier Macmillan International Editions copy and the paper is, naturally, yellowish. This is a simple printing using only black fonts and gray boxes for definitions and theorems. Can you imagine a Statistics textbook with no pink, red, purple, green, and blue popping into your eyes from every corner of the page? Texbooks used to be this simple (and affordable) before most of today's college students were even born. For example, all chapter exercises (not only the odd-numbered ones) are answered at the end of the book. I guess the editors in the seventies weren't smart enough to come up with the best way of liberating students' pokets from the occupation of the mighty dollar: don't provide the answers in the book but print a Student Guide instead. Even the newer editions of this book use this approach.

How about the rest of the book? The second edition is a a decent book, concise and clean. The presentation is a bit dry by today's cluttered standards, but I found enough examples to illustrate the concepts and results. The material is well organized, with the mathematical expectation presented together with standard deviation and variance, as it should be. The section on joint probability distributions is one of the best I've seen. Some notations are a bit dated but you will have no problem understanding them.

So I think you will be able to learn probability and statistics from this book by yourself. Unless you're some straight A senior math college student who's never heard of permutations and combinations.

How about the rest of the book? The second edition is a a decent book, concise and clean. The presentation is a bit dry by today's cluttered standards, but I found enough examples to illustrate the concepts and results. The material is well organized, with the mathematical expectation presented together with standard deviation and variance, as it should be. The section on joint probability distributions is one of the best I've seen. Some notations are a bit dated but you will have no problem understanding them.

So I think you will be able to learn probability and statistics from this book by yourself. Unless you're some straight A senior math college student who's never heard of permutations and combinations.

35 of 36 people found the following review helpful

Compared to the average book on this subject on the market, this book is a gem. When you add its very low price (as I write this, a used copy is under $2 + shipping on Amazon.com), you get two gems. You count how many gems you get when you add these points:

1) It presents the version of the probability theory that is firmly based on sample spaces. Consequently, and very importantly, a random variable is defined as a real-valued function on a sample space, which makes a lot more sense than the typical definition you will find in the terribly overpriced, overcolored and overly dumbed down modern college-statistics books, in which a random variable is defined as a "variable (a concept that is not defined in these books, btw) that takes chance values". Actually, Goldberg tells you that the name "random variable" is singularly inappropriate for something that is not random, nor a variable. You will also learn the Bayes' theorem, which is shamefully placed in footnotes or even completely omitted by many all-shiny new books.

2) This guy can teach and so can his book! It will take you from step one to step one hundred without ever giving you the impression that he's just skipped a few steps in a hurry to get to the next topic. It does not jump ahead, and it does not lag either: you won't find tons of examples and exercises that add nothing to the previous ones but make the book thicker and more expensive.

Each example and exercise has a purpose, either to introduce a new concept or a particular case or to make you use another theorem to solve it. There are no hard or tricky exercises here, you only need to have read the section carefully. Almost every theorem or definition is introduced or followed by one example or two. Goldberg tells you all and only what you need to know to fully understand what you are doing; no more, and no less.

I found only one bug: a few concepts are introduced by even-numbered exercises at the end of the sections. Given that the book provides answers only to odd-numbered exercises, you cannot verify your understanding of those concepts. Fortunately, there are only one or two such cases in each section, and those concepts are not needed later in the book.

Also, don't forget that this is a book on discrete probability only. There is no place for the normal distribution, nor for any topics in statistics, apart from a formal introduction to populations and samples with replacement, both of which can be defined using random variables and their distributions -- did you know that?

1) It presents the version of the probability theory that is firmly based on sample spaces. Consequently, and very importantly, a random variable is defined as a real-valued function on a sample space, which makes a lot more sense than the typical definition you will find in the terribly overpriced, overcolored and overly dumbed down modern college-statistics books, in which a random variable is defined as a "variable (a concept that is not defined in these books, btw) that takes chance values". Actually, Goldberg tells you that the name "random variable" is singularly inappropriate for something that is not random, nor a variable. You will also learn the Bayes' theorem, which is shamefully placed in footnotes or even completely omitted by many all-shiny new books.

2) This guy can teach and so can his book! It will take you from step one to step one hundred without ever giving you the impression that he's just skipped a few steps in a hurry to get to the next topic. It does not jump ahead, and it does not lag either: you won't find tons of examples and exercises that add nothing to the previous ones but make the book thicker and more expensive.

Each example and exercise has a purpose, either to introduce a new concept or a particular case or to make you use another theorem to solve it. There are no hard or tricky exercises here, you only need to have read the section carefully. Almost every theorem or definition is introduced or followed by one example or two. Goldberg tells you all and only what you need to know to fully understand what you are doing; no more, and no less.

I found only one bug: a few concepts are introduced by even-numbered exercises at the end of the sections. Given that the book provides answers only to odd-numbered exercises, you cannot verify your understanding of those concepts. Fortunately, there are only one or two such cases in each section, and those concepts are not needed later in the book.

Also, don't forget that this is a book on discrete probability only. There is no place for the normal distribution, nor for any topics in statistics, apart from a formal introduction to populations and samples with replacement, both of which can be defined using random variables and their distributions -- did you know that?

17 of 19 people found the following review helpful

Update for the 2009 edition: Mr Lutz has really gone overboard. This "learning" book has became heavier than Python itself. The size of the book detracts from the spirit of Python, which fortunately remains a compact and simple language as intended. I believe that many computer-literate people will find it easier and definitely quicker to just start coding in Python and read a documentation page now and then rather than read this book. Busy programmers who reported writing their first useful Python class in a few hours would need weeks to do the same while reading this book. Mr Lutz, let's be pythonic and scale this tome back to 500 pages, shall we?

The rest of the review is about the second edition:

I would never try to use this book as a reference. It was not designed and it's not good for that.

It was designed as your first book on Python, especially if this is your first programming language. As such, it gives you a really thorough and extensive introduction written by a renowed authority. The parts on functional programming, Python's OOP and modules lay the solid foundation for the future Python programmer. Beware though: compared to similar "foundation" books in other languages' realms, this one is slow-paced, limited in scope, wordy and even redundant at times.

If you already know a language like C++, Java or Perl, and especially if you've already written some Python code, then this book is not your best choice: it will seem terribly slow paced, tedious, bloated and of no value as a reference (which is what an experienced programmer like you really needs most of the time). In this case, you could use a short and freely available tutorial like Guido's, then a good reference book like Python in a Nutshell and maybe some more advanced books like Python Cookbook and Python 2.1 Bible (provided there will be a new edition).

As an intermediate or experienced programmer, you may still benefit from Lutz's "textbook". You may want to skim quickly through the first 3 Parts (which make 180 pages of beginner's stuff you've learned in highschool, decorated with the occasional gem toward the end of some chapters), then slow down a bit for the rest of the book and pay special attention to chapters 14, 17, 18, 21, 22, 23, and 27. This book has too many chapters for my taste, btw.

Part VIII, written by another authority (David Ascher), is a little too short and still bad for reference. In the next edition, I hope it will be expanded to a reasonable level of detail. I found the coverage of regular expressions particularly disappointing -- probably because they are covered by Mr Lutz's other book, Programming Python, which was supposed to be your second book. The exercises at the end of each Part are not the most interesting and useful I know of.

The rest of the review is about the second edition:

I would never try to use this book as a reference. It was not designed and it's not good for that.

It was designed as your first book on Python, especially if this is your first programming language. As such, it gives you a really thorough and extensive introduction written by a renowed authority. The parts on functional programming, Python's OOP and modules lay the solid foundation for the future Python programmer. Beware though: compared to similar "foundation" books in other languages' realms, this one is slow-paced, limited in scope, wordy and even redundant at times.

If you already know a language like C++, Java or Perl, and especially if you've already written some Python code, then this book is not your best choice: it will seem terribly slow paced, tedious, bloated and of no value as a reference (which is what an experienced programmer like you really needs most of the time). In this case, you could use a short and freely available tutorial like Guido's, then a good reference book like Python in a Nutshell and maybe some more advanced books like Python Cookbook and Python 2.1 Bible (provided there will be a new edition).

As an intermediate or experienced programmer, you may still benefit from Lutz's "textbook". You may want to skim quickly through the first 3 Parts (which make 180 pages of beginner's stuff you've learned in highschool, decorated with the occasional gem toward the end of some chapters), then slow down a bit for the rest of the book and pay special attention to chapters 14, 17, 18, 21, 22, 23, and 27. This book has too many chapters for my taste, btw.

Part VIII, written by another authority (David Ascher), is a little too short and still bad for reference. In the next edition, I hope it will be expanded to a reasonable level of detail. I found the coverage of regular expressions particularly disappointing -- probably because they are covered by Mr Lutz's other book, Programming Python, which was supposed to be your second book. The exercises at the end of each Part are not the most interesting and useful I know of.