Haskell Data Analysis Cookbook - a Book Review

As with my previous post, Clojure Data Analysis Cookbook - a Book Review, I was this time offered to review Haskell Data Analysis Cookbook by Nishant Shukla.  First impressions: those are two very similar and related books that have some overlapping ideas, but not only are the programming languages used totally different in "genre", the content itself also cover some different data analysis grounds and could be treated as complementary books in that way.

The book itself is very example oriented (much like the Clojure Data Analysis Cookbook), basically being a collection of code recipes for accomplishing various common tasks for data analysis.  It does give you some quick explanations of why and what else to "see also".

It gives you recipes to take in raw data in the form of CSV, JSON, XML, or whatever, including data that lives on web servers (via HTTP GET or POST requests).  Then there are recipes to build up datasets in MongoDB, or SQLite databases.  To recipes to clean up that data, do analysis (e.g. clustering with k-means), to visualizing, presenting, and exporting that analysis.

Each recipe is more or less self-contained, without much in building on top of previous recipes.  It makes the book more "random access".  It's less a book to read through cover to cover, and more of a handy reference to use by full-text searching for key terms, clicking on the relevant topic in the table of contents, or by looking up terms in the index.  It's definitely a book I'd rather have as a PDF ebook so that I can access it anywhere in the world, and so I can do full-text search in.  It does come in Mobi as well as ePub formats, and code samples are provided in a separate zipped download as well.

Having said that, you can tell whether a book was made to be seriously used as a reference or not by looking at its index.  There are 9 pages of indices, equivalent to about 2.9% of the number of pages previous to the index.  This book can certainly be used as a reference.

As a reference book, it's great for people who have already a familiarity with Haskell in general.  If you don't know Haskell, this book won't teach it to you.  That is, unfortunately, possibly a missed marketing opportunity, as those who don't know Haskell (but have knowledge of another programming language) really only needs a small bit to understand enough of how functions are written in Haskell to pick up what's going on in the book.  This means if you know another programming language, know a bit about data analysis, you could use this book to learn some Haskell so long as you pick up the basic syntax with another tutorial in hand (so it's really not a show stopper to using this book).

Similarly, I'd say you had best be familiar with how to do data analysis as a discipline in itself.  If you don't know whether to do clustering or regression, or whether to use a K-NN or K-means, this book won't teach it to you.

Much of that is, of course, echoing the Clojure Data Analysis Cookbook.  Where the Haskell Data Analysis Cookbook differs, makes the two books have a set of complementary ideas.  Whereas both books talk about concurrency and parallelism, the Clojure DAC goes into those topics (including distributed computing) in much more detail.

On the other hand, whereas both books talk about preparing and processing data (prior to performing statistics or machine learning on it), the Haskell DAC goes into much more detail on topics like processing strings with more advanced algorithms (as in computing the Jaro-Winkler distance between strings, not like doing substring/concat operations), computing hashes and using bloom filters, and working with trees and graphs (as in node-and-link graph theory graphs, not grade-school bar graphs).

So in some sense, the Haskell Data Analysis Cookbook has more theory heavy topics (graphs and trees!), whilst the Clojure Data Analysis Cookbook has more "engineering" topics (concurrency, parallelism, and distributed computing).

Neither books are comprehensive treatise on the topic, but someone who needs a practical refresher on working with graphs and trees may find Haskell Data Analysis Cookbook to be quite useful.

All in all, I'd say this is a decent book, because if you have some familiarity of Haskell, have some familiarity with some of the basic technologies like JSON, MongoDB, or SQLite, have taken a class or two of data analysis or machine learning in university (or a MOOC?), and aren't expecting a lot of hand holding from the book, then this book is a great guide to start you off to doing some data analysis with Haskell.

Clojure Data Analysis Cookbook - a Book Review

Like yogthos, I was recently asked to review Clojure Data Analysis Cookbook.  With Incanter, data analysis has been one of the "selling points" of Clojure as a practical language.  A practical lisp for practical data analysis.

(Edit 2016: a second edition is available!)

The book is very example oriented, basically being a collection of code recipes for accomplishing apparently common tasks for data analysis.  It gives you recipes to go from taking raw data in the form of CSV, JSON, or whatever, to making an Incanter dataset, to doing analysis on those datasets (e.g. clustering the data by using a self-organizing map), to saving, viewing, or charting the resultant data.  Each recipe is accompanied by brief explanations, and cross-references to other related recipes in the book.

Each recipe is more or less self-contained, without much in building on top of previous recipes.  It makes the book more "random access".  It's less a book to read through cover to cover, and more of a handy reference to use by full-text searching for key terms, clicking on the relevant topic in the table of contents, or by looking up terms in the index.  It's definitely a book I'd rather have as a PDF ebook so that I can access it anywhere in the world, and so I can do full-text search in.

Having said that, you can tell whether a book was made to be seriously used as a reference or not by looking at its index.  There are 10 pages of indices, equivalent to about 3.2% of the number of pages previous to the index.  This counts as a book to be seriously used as a reference.

As a reference book, it's great for people who have already a familiarity with Clojure (and better yet, Incanter) in general.  If you don't know Clojure, this book won't teach it to you.  If you don't know Incanter, you can pick it up from this book if you're a fast learner (don't expect a lot of hand holding in learning Incanter though).

Similarly, I'd say you had best be familiar with how to do data analysis as a discipline in itself.  If you don't know whether to do clustering or regression, or whether to use a SOM or K-means, this book won't teach it to you.

Also, as a reference book, it is not comprehensive.  For example, as far as neural networks go, it only includes self-organizing maps.  There are no other kinds mentioned.  If you want another kind of neural network, you best know where to look for another Java or Clojure library.

Even with all those caveats, I'd still say this is a pretty decent book.  Why?  Because if you have some familiarity of Clojure, played around with Incanter for a bit to learn that library, have taken a class or two of data analysis in university, and aren't expecting a lot of hand holding from the book, then this book is a great guide to start you off on the road to doing data analysis with Clojure, Incanter, Weka, OpenCL, Cascalog, etc.

What Latin Phrases to Italicize in Formal Writing?

When writing formally, you'll probably have to use some Latin or other foreign words or phrases, like "e.g.", "viz.", "inter alia", "etc.", etc. Do you italicize them? Or do you not? Actually, some you do italicize, while others you don't. So how do you figure out which is which?

The rule of thumb is, if the word or short phrase has been so commonly used in English writing that it's immediately recognizable to an English reader, meaning the word or phrase has been assimilated into current English usage, then don't italicize it (i.e. it is anglicized and should be set in roman). If the phrase is an abbreviation of a Latin phrase, also don't italicize it. Finally, don't italicize foreign proper names, foreign names of people, institutions, places, etc., and also usually not for foreign quotation.

Otherwise, italicize the word or phrase to show that it's foreign. When writing italicized foreign words or phrases, make sure to put in the appropriate accents and diacritical marks (this means that for German nouns, the initial letter is capitalized.

But which words or short phrases are commonly used in English enough not to be italicized? It could be a tough call. To help you, here's a compiled listing of frequently used foreign phrases and whether to italicize them:

Few notes on ergonomic keyboards and mouse

I'm typing away at a lot of software programming and thesis writing, so having a good keyboard is a must. Here's two suggestions, both of which I've personally tried and has worked well for me. I wrote down a few notes for you to ponder.

Currently, I use a Microsoft Comfort Curve Keyboard 2000. It's an ergonomic keyboard with a soft curve laterally, and no vertical curve (it lays flat). It's a wicked keyboard if your shoulder width is not so wide, because it has only that slight curve, rather than the dramatic curve of other ergonomic keyboards.  It also has a light but distinctive touch, so it's easy to press the key down, but not so easy that you would accidentally "smoosh" a key down by accident.  The key's travel — how far it travels from the start of pressing a key to bottoming out — is also quite short, so there's less finger motion required to type.

If the Comfort Curve 2000 doesn't fit your body type, you might want to try the Microsoft Natural Ergonomics Keyboard 4000.  I've used that one before, and it's pretty good, but only if your shoulder width is wider than, well, mine is.  Unfortunately, it's keys have a longer travel length, and its spacebar is a tad heavy to press down due to its design. If you're not used to the keyboard, your fingers may become tired and sore the first few times of prolonged usage, but at least for me, I get used to it soon after.  I've used that for years before switching to the Comfort Curve 2000 model, and it was a good experience.

Unfortunately, both of those keyboards, at least when used on a Mac, optionally suggests that you install the Microsoft IntelliType software. The IntelliType software has been known to apparently cause a lot of kernel panics for on Mac OS X — on the order of once or twice a day even, when under heavy typing load. If you use Windows, then this is not an issue (I've used both of those keyboards under Windows XP and Windows 7, and they work well). If you use a Mac, you may find my previous post on the issue useful.

Mac OS X Kernel Panics due to Microsoft IntelliType: Solution

It seems all evidence [1] points to Microsoft IntelliType causes kernel panics on Macs.  This seems to be a problem that has existed for a long while, from IntelliType versions 7 at least up to 8, and Mac OS X from 10.5 at least up to 10.6.

I learned this the hard way, having bought a Microsoft Comfort Curve 2000 ergonomic keyboard. That's a wicked keyboard, by the way, if your shoulder width is not so wide, because it has only a slight curve, rather than the dramatic curve of other ergonomic keyboards.  It also has a light but distinctive touch, so it's easy to press the key down, but not so easy that you would accidentally "smoosh" a key down by accident.  The key's travel is also quite short, so it's less finger motion required to type.

Unfortunately, installing the IntelliType software seems to have caused kernel panics for my MacBook Pro on the order of once or twice a day (under heavy typing load.  That meant I lost a lot of programming and typing of emails and reports.  sigh

The solution is simple: uninstall IntelliType.  The uninstaller is in the Utilities folder in the Applications folder.  But that's not a very good solution by itself, because the Command/"Apple" and Option keys are reversed on Microsoft keyboards compared to Mac keyboards.  So here's the other half of the solution:

Can you get success without hard work? On average, no.

It's fascinating how the importance to success that long hours of hard work is, is so often missed or dismissed.

In "Your Child Left Behind", Ripley forwards the notion that standardized tests and curriculum is important to improving education in the USA.  And that, in fact, it's more important than controlling the inputs to education (money spent, teacher/student ratio, etc).

Now, I don't doubt the importance of standardized tests and curriculum at all.  What I was actually struck by in that essay was this comparison:

Last year, a study comparing standardized math tests given to third-graders in Massachusetts and Hong Kong found embarrassing disparities. Even at that early age, kids in Hong Kong were being asked more-demanding questions that required more-complex responses.

This comparison seems to imply something about standardized curriculum and tests, but it completely ignores a major difference in the input to the education system of Hong Kong.  That of private tutors.  Private tutoring in Hong Kong is a big business, and more than half of Hong Kong's students get private assistance outside of school [1].  This is something that is pervasive, occurring at almost all grade levels [2].

My sense is that tutoring is still not a major contributor to the education "system" in the USA, and certainly not in Canada.  Based on my own experience in Canada, I know a fair portion of teachers even actively discourage the students and families from seeking tutors.  This difference in outside of school, tutoring style, input to students' education is an absolutely important difference.

Working out the numbers, if a student attends an hour of tutoring a week for, say, 40 weeks of schooling, that works out to an extra almost 7 weeks of focused, adult supervised and assisted learning.  Many students in Hong Kong attend tutoring sessions more frequently than that.

Book Review: Art and Science of Teaching

The Art and Science of Teaching by Robert Marzano is subtitled, "A Comprehensive Framework for Effective Instruction." I would suggest emphasizing "Framework" in that description over "Comprehensive", but that is not to say the book is bad. In fact, I recommend this book, especially for the big ideas it discusses in some detail on effective teaching.

I've been meaning to write a brief review of this book as it's been given to me as a gift a long time ago.  It's a solid book of big ideas with adequate examples of how those ideas might be implemented in a classroom.  Ideas regarding, for example, how a teacher can "establish and maintain effective relationships with students," and how to "help students practice and deepen their understanding of new knowledge."

The ideas are great, and examples are good, and the ideas are backed up by research (that's the science part of the book).  I'd love to try employing more of these ideas into my own classroom in the future.  There are, however, two major flaws in the book I should mention.

Book Review: Introduction to Algorithms

Introduction to Algorithms, by Cormen, Leiserson, Rivest, and Stein, is a good introductory textbook to algorithm design and analysis for the undergraduate student in their first course on the study of algorithms.

It is a good learning text in that the writing style offers more explanations and has a more "conversational" tone (as compared to other texts that are much more succinct). Having said that, this is still a textbook and assumes students to have a fairly decent math background and ability to think in a mathematical manner.

It would not be far off to say that this is more of a math text than a programmers' text. That is to say, this is not a cookbook with code that can be copied to make things work. This is a text to help students learn to think and reason about algorithms.

For a first course in formal algorithms for undergraduates, this would be a good text. If you want a more rigorous, mathematical, and succinct reference or introduction, Algorithm Design by Kleinberg and Tardos (which I have reviewed) may be a better fit for you.

Book Review: Algorithm Design

Algorithm Design by Kleinberg and Tardos is a good handbook on algorithms, the best I've seen so far in terms of balancing theoretical understanding and practical concerns. Examples that are given often have a realism lacking in other texts, showing a concern for practitioners who may want to see how these algorithms could actually be useful.

On the other hand, the succinctness of the writing style meant I had to read passages several times to understand fully the concepts being explained (ie, it required a close reading, as we might say in philosophy writing). This is, I think, a good thing: rereading well written passages is more effective than reading repetitious passages once through anyway.

Especially in the chapters regarding NP completeness and approximation algorithms, the "tone" of the writing definitely turned much more mathematical, and that's especially when a good mathematical background is helpful.

Largely because of the succinctness of the writing style and assumed mathematical background, I would say this is more of an advanced upper-undergraduate level textbook, or a good introductory text at the graduate level. I'm afraid at the introductory level for undergraduates, this text may serve more to instil confusion (a better text at the undergraduate introductory level may be Introduction to Algorithms, which I also reviewed).

If you're looking for an advanced algorithms textbook, I would definitely recommend it.

Book Review: Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach by Stuart Russell (Professor of Computer Science at University of California,
Berkeley) and Peter Norvig (Director of Research at Google) is the standard introductory textbook to AI theory and application at the undergraduate level.

It's friendly in the sense that it requires less mathematical maturity, and assumes less mathematical background. It does have a very comprehensive and broad survey of the entire field, which is good in that it gives the student a sense of the entire field, but bad in that there is much less depth in any single area.

If you are looking for a single good introductory textbook to get started into research, there are probably better alternatives (such as Pattern Recognition and Machine Learning, which I also reviewed). But if you are looking for a single good introductory textbook to understand the field better, and not necessarily to prepare to do original research, this is probably a great book to start with and I recommend it highly.

A word of caution of the term "artificial intelligence" though. Much of the algorithms and techniques referred to in this text are probably more accurately described as machine learning or statistical pattern recognition algorithms. If you are looking for a book on artificial general intelligence, you will be disappointed.

Book Review: Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning is by Christopher Bishop (Chief Research Scientist at Microsoft Research Cambridge, and Professor of Computer Science at the University of Edinburgh).

It's a heavy read on machine learning algorithms in that it is very math intensive. It would not be too far off to call this an applied math textbook rather than a computer science textbook, and it is definitely aimed at the graduate level student with a good amount of mathematical maturity.

Having said that, if you are a machine learning researcher in need of learning all sorts of mathematical details of the algorithms you are using, this book is a good place to start. It is by no means a complete reference, but certainly a good introduction at the graduate level.

If you are an undergraduate student, there are other textbooks available that might be more suitable (such as Artificial Intelligence: A Modern Approach, which I also reviewed). If you just want cookbook style text on the libraries you could use to employ machine learning algorithms, there are probably other resources that would be more suitable.

If you are starting out in researching machine learning algorithms, or are using it as a large part of your computing science research, this is the right book. Just be prepared to learn and use a lot of math!

Mouse Speed and Sensitivity Settings in Ubuntu

This is a story about the xset utility, that ends with a twist! And it's about my trackball, not my mouse.

I have a Kensington TurboBall Trackball, the best trackball on the planet (so I say), but had an extremely slow speed when I used it in Ubuntu.

You'd think to fix that, I'd just have to open up the Mouse System Preferences panel and increase the speed or sensitivity. Well I did that, and nothing happened. Who knows why.

So I started looking through the Ubuntu OS manual (ie, Google) to see if there's a way to fix this. I come up with things about changing a bunch of code in the xorg.conf file, but I really don't want to have to do that. Not that I couldn't, but for me, the less fiddling around with the configuration files the better. Plus I read somewhere it wouldn't work with the latest Ubuntu releases as it's using the HAL for that sort of stuff now. More config files I don't want to fiddle with.

Book Review: "Coders at Work"

Coders at Work is for programmers and computing scientists interested in how some very well known coders think about their art or craft. It is a series of interviews of some of the top coders of our time. It includes their thoughts on some very controversial issues on coding style, algorithm development, testing, documentation, etc.

It's a great book to read to get a sense of how some of these people think about those issues, and it's incredibly rewarding if you already know the names of some of the coders interviewed. I was especially excited to read the interviews of Peter Norvig, Guy Steele, Ken Thompson, and Donald Knuth. The other interviews were great too, and better if you already know something about those people.

Now just because some of these people think one way or another on an issue doesn't mean I would go and imitate them. The main thing is to read their explanation of what they're thinking, the why's and how's, and examine it to understand how they're thinking about an issue. Now how they think about issues — that is possibly worthy of imitation.

Definitely recommended for programmers and computing scientists wanting to improve their game!

Book Review: "Hyperspace: A Scientific Odyssey Through Parallel Universes, Time Warps, and the 10th Dimension"

Wow, what can I say about this book? It inspired me to learn more science, specifically physics. Hyperspace: A Scientific Odyssey Through Parallel Universes, Time Warps, and the 10th Dimension has vivid analogies that I was able to, more or less, understand when I was about 11 years old.

The thought experiments Kaku talked about made me imagine the possibilities of the universe. It's a great book to get kids interested in science since it explains the intuitions without being stuck in the precise details.

Intuitions of what? Well, intuitions of space, time, and ultimately spacetime of more than three dimensions. It has nice thought experiments to help develop intuitions of what it's like to have more than three dimensions. Kaku also explains the intuitions around string theory, which is fascinating as well (even if it turns out that string theory is wrong, it's still fun to imagine the universe in a different way).

A great book, especially for kids. Highly recommended!

Book Review: "Philosophy of Mind"

Philosophy of Mind (Dimensions of Philosophy) is a fantastic introduction to the subject, and to the basic fundamental issues surrounding any topic that relates to the mind, including cognitive science, artificial intelligence as it relates to cognitive science, psychology, etc.

It covers a lot of ground without presuming specialist knowledge of terminologies, and it briefly explains all related concepts that are required to understand the issues at play (eg, concepts from as far afield as philosophy of science, language, or the computing science concept of a Turing machine).

Book Review: "Difficult Conversations: How to Discuss what Matters Most"

Difficult Conversations: How to Discuss what Matters Most is a good book offering an objective analysis of what goes on and into difficult conversations. Difficult conversations are those on topics that may be difficult and uncomfortable because of its ability to cause pain and negative, if not destructive, results.

By dissecting such conversations into three pieces that people can affect, viz, the "what happened" conversation, the "feelings" conversation, and the "identity" conversation, this book provides recommendations on how to diffuse and create constructive conversations instead.

This book provides examples of what to do and what not to do in some realistically common situations and this improves readers' understanding of the theories they lay out. The focus on actionable behaviours makes this book useful and insightful for the general audience, rather than merely inspirational.

Book Review: "Religion and Science"

Religion and Science (Gifford Lectures Series) is an excellent introduction to historical and contemporary issues and views in the interaction of science, religion, and society.

Very detailed in its presentation of scientific topics, although for those who are already well versed in various popular general scientific topics, certain sections describing relativity, quantum theory, astronomy, etc, might be a bit repetitive.

The same could probably be said of the theological topics for those already well versed in them. That is, of course, why this book makes for an excellent introductory text.

This book is not a one or two sided view but instead presents many of the nuances in the interface between science and religion in society. I recommend this book, and especially more so in conjunction with Midgley's Science as Salvation: A Modern Myth and its Meaning, which I've reviewed as well.

Book Review: "Science as Salvation: A Modern Myth and its Meaning"

Science as Salvation: A Modern Myth and its Meaning is a fantastic book about the development of science and its advancement in Western culture. It contains a lot of deep and thought provoking discussions on the position science holds in current Western culture, and its metaphysical and epistemological nature.

In particular, it looks at the notion of science as a societal, cultural, and even spiritual force, a notion that is sometimes thrust onto science and perpetuated by certain science writers. To some, the building of this notion tends towards using science as part of a myth constructed by people to shape and understand how to live in an age of an over abundance of information; perhaps it even seems to be displacing the role of religion.

Neither entirely anti-science nor pro-science, Midgley presents a nuanced view of the interaction of science, religion, and society. Midgley encourages a rethinking of the role and importance of science, and its relationship to how we live and how we ought to decide to live.

On the whole, this is a book I recommend on the philosophy of science and society. I also recommend reading this in conjunction with Barbour's Religion and Science (Gifford Lectures Series), which I will be reviewing as well.

Book Review: "Blink: The Power of Thinking Without Thinking"

Malcolm Gladwell's Blink: The Power of Thinking Without Thinking presents some interesting ideas and anecdotes to help better understand how good decisions and snap judgements are made when people are faced with overwhelming data.

Plenty of skepticism ought to accompany you as you read this book. The stories are well written and may seduce you into believing his theories all too easily. Pack along some salt for a better taste. (If you are thinking of reading Think!: Why Crucial Decisions Can't Be Made in the Blink of an Eye, you might want to read my review of that one first.)

Especially important is to look into his explanation of how, why, and when the snap judgements worked in the cases he presents. He doesn't make the absurd claim that all snap judgements in all cases under all circumstances are better than thought out judgements. If you've carefully read Gladwell's Outliers: The Story of Success (which I've reviewed previously), you may notice that Gladwell's point in the narratives he shares are often subtle and sometimes easy to misinterpret. Maybe that's why it makes for such an enjoyable and engaging read!

Artificial Intelligence Needs to Learn to Read

To complete my previous post on the danger of artificial intelligence, I'll now briefly explain why I believe AI systems needs the ability to read and understand the written word.

The problem of building an intelligent AI system that can cause so much harm is actually nothing new in the sense that parents face this problem all the time. Why do parents not fear their children growing up to abandon them or worse? The problem with building autonomous systems is that they are autonomous, so before we set them lose, we might want to think about how to ensure these systems will autonomously decide not to harm us.