Thursday, May 31, 2012

Statistics for People Who (Think They) Hate Statistics

Statistics for People Who (Think They) Hate Statistics

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Product Description

"Your book has changed my perception and attitude towards statistics. It is very well written, the language is completely user friendly and the progression of new concepts within each chapter is fantastic. Although my class is only on chapter 5 at this point, I have found my interest in the concepts and order in which you introduce them becoming greater. I thank you with very much appreciation as I was someone who dreaded taking statistics and thought I hated it!" - Jessica Maes, graduate student

The Fourth Edition of Neil J. Salkind� �s bestselling text introduces students to the fundamentals of basic statistics in an informative, personable, and unintimidating way. The author expertly guides students through various statistical procedures, beginning with basic descriptive statistics and correlation and graphical representation of data and ending with inferential techniques including analysis of variance.

Statistics for People Who (Think They) Hate Statistics Review

This book has been quite a challenge. The material itself is not hard and is well explained. The only problem is ALL THE ERRORS IN THE BOOK!! There are so many errors, it nearly makes it impossible to learn. Every chapter you have to go to the website to look up the correct formulas or data sets or answers for the back of the book. Incredibly frustrating. I'm just trying to do my work and learn for grad school. This book is not helping me keep my stress level down. The idea for the book is good. When the errors get corrected, the information will be very helpful.

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Wednesday, May 30, 2012

Informatics and Nursing

Informatics and Nursing

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This comprehensive text reviews the concepts and applications of nursing informatics. Starting with an introduction to nursing informatics, the contents proceed from Unit One which covers computer basics to Unit Five which examines skills and techniques for using the end product of informatics: data. Other units review the effects that the Internet is having on healthcare, the ins and outs of using personal productivity software, and informatics in the clinical area.

Informatics and Nursing Review

The content was good, but I was unable to go direclty to page numbers in the kindle version, only chapters.

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Tuesday, May 29, 2012

Pharmacology (Lippincott's Illustrated Reviews Series)

Pharmacology (Lippincott's Illustrated Reviews Series)

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Lippincott's Illustrated Reviews: Pharmacology, Fifth Edition enables rapid review and assimilation of complex information and focuses on the essentials of medical pharmacology. Clear, sequential pictures present mechanisms of action and actually show, rather than tell, students how drugs work. This book features a signature outline format with more than 500 full-color illustrations and cross-references to other volumes in this bestselling, student-oriented series.

Pharmacology (Lippincott's Illustrated Reviews Series) Review

I used the earlier editions while studying in medical school and for reference since. This new edition, while more voluminous, is unwieldy and confusing. The cute little icons that used to be so helpful are now just unclear, superfluous and often just silly. This is a good example of a publisher working too hard to make a text better, and ruining a good thing in the process. A real disappointment...

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Monday, May 28, 2012

The Brewers Association's Guide to Starting Your Own Brewery

The Brewers Association's Guide to Starting Your Own Brewery

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Starting a successful brewery takes more than heart. The Brewers Association’s Guide to Starting Your Own Brewery delivers essential industry knowledge to brewers aspiring to chart their own course. While America s craft beer renaissance continues, emphasis must remain on producing the highest quality beer—or the success of the entire industry is jeopardized. This comprehensive guide will help you plan and open a thriving, quality-oriented brewery. It reviews everything that matters, from site selection and branding to regulatory requirements, flooring choices and equipment considerations. Industry veteran Dick Cantwell of Elysian Brewing adeptly covers ingredients, financing, business plans, quality assurance, distribution, wastewater, sustainability practices and more, for prospective brewpub and packaging brewery owners alike. Cantwell walks the reader through the planning and execution required to turn craft brewing dreams into reality.

The Brewers Association's Guide to Starting Your Own Brewery Review

I'm not sure that "Guide" is the correct description for this book. It reads more like a narrative of the author's experiences, experiences of others in the industry, and history of the craft brewing industry. A very enjoyable read, but far from detailed enough or specific enough to be a primary reference for opening a brewery. It is, however, full of practical advice which the author shares from his personal experiences and/or examples from other breweries.

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Sunday, May 27, 2012

Theoretical Basis for Nursing

Theoretical Basis for Nursing

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Designed for one of the core courses taught in all graduate nursing programs, this text is the most contemporary, concise, and readable nursing theory textbook on the market. It presents historical perspectives on the development of nursing theory, assessments of concept and theory development and theory evaluation, middle-range theories, and shared theories from other disciplines in the sociologic, behavioral, and biomedical sciences, focusing on the application of theory. Learning features found throughout the text include case studies and end-of-chapter summaries that help to reinforce essential concepts.

Theoretical Basis for Nursing Review

It wasn't too boring, but was bland to read. I thought the authors were redundant and overly opinionated on certain topics. Its a "teaching" text, and the authors came across as providing their view rather than objective.

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Saturday, May 26, 2012

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) Review

(Disclaimer: I have worked with a draft of the book and been allowed to use the instructor's review copy for this review. I have bought the book from Amazon.co.uk, but apparently this Amazon.com review can't be tagged "verified purchase". I don't receive any compensation whatsoever for writing this review. I hope it will help you chose a machine learning textbook.)

Similar textbooks on statistical/probabilistic machine learning (links to book websites, not Amazon pages):
- Barber's Bayesian Reasoning and Machine Learning ("BRML", Cambridge University Press 2012)
- Koller and Friedman's Probabilistic Graphical Models ("PGM", MIT Press 2009)
- Bishop's Pattern Recognition and Machine Learning ("PRML", Springer 2006)
- MacKay's Information Theory, Inference and Learning Algorithms ("ITILA", CUP 2003)
- Hastie, Tibshirani and Friedman's Elements of Statistical Learning ("ESL", Springer 2009)

* Perspective: My perspective is that of a machine learning researcher and student, who has used these books for reference and study, but not as classroom textbooks.

* Audience/prerequisites: they are comparable among all the textbooks mentioned. BRML has lower expected commitment and specialization, PGM requires more scrupulous reading. The books differ in their topics and disciplinary approach, some more statistical (ESL), some more Bayesian (PRML, ITILA), some focused on graphical models (PGM, BRML). K Murphy compares MLAPP to others here. For detailed coverage comparison, read the table of contents on the book websites.

* Main strength: MLAPP stands out for covering more advanced and current research topics: there is a full chapter on Latent Dirichlet Allocation, learning to rank, L1 regularization, deep networks; in the basics, the decision theory part is quite thorough (e.g. will mention Jeffrey's/uninformative priors). The book is "open" and vivid, doesn't shy away from current research and advanced concepts. This seems to be purposeful, as it shows in many aspects:

- quotes liberally from web sources, something usually not done in academic publications

- borrows "the best" from other authors (always with permission and acknowledgment, of course): most importantly the best pictures and diagrams, but also tables, recaps, insightful diagrams. Whereas other books will produce their own pictures and diagrams themselves (eg, PRML has a distinctive clarity and style in its illustrations), MLAPP takes many of its colour illustrations from other people's publications; therefore it can select the most pithy and relevant pictures to make a point. You could think that reproductions may be illegible and require extra effort to interpret because they come from a variety of sources; I have found that the bonus coming from having precisely the right image prevails.

- frequent references to the literature, mentions of extensions and open questions, as well as computational complexity considerations: for instance, the section on HMMs will mention duration modeling and variable-duration Markov models, and a comparison of the expressive power of hierarchical HMMs versus stochastic context-free grammars, complete with relevant citations, and a brief mention of the computational complexity results from the publications. All this connects the material with research and new ideas in a fine way -- which other textbooks don't achieve, I find. For instance, PGM defers references to a literature section at the end of each chapter, resulting in a more self-contained, but more poorly "linked" text.

* Didactic aids: Another distinctive feature is that the author clearly has tried to include didactic aids gathered over the years, such as recaps, comparative tables, diagrams, much in the spirit of the "generative model of generative models" (Roweis and Ghahramani): e.g. table comparing all models discussed, pros and cons of generative vs. discriminative models, recap of operations on HMMs (smoothing, filtering etc), list of parameter estimation methods for CRFs.

* Editorial features: Other editorial features worth mentioning are

- compared to others, helpful mentions of terminology, e.g. jargon, nomenclature, concept names, in bold throughout the text ("you could also devise a variant thus; this is called so-and-so")

- mathematical notation relatively clear and consistent, occasional obscurities. PGM stands out as excruciatingly precise on this aspect.

- boxes/layout: no "skill boxes" or "case study boxes" (PGM), not many roadmap/difficulty indications like ITILA or PGM, examples are present but woven into the text (not separated like PGM or BRML). Layout rather plain and homogeneous, much like PRML.

- sadly lacks list of figures and tables, but has index of code

* Complete accompanying material:

- interesting exercises (yet fewer than PRML, BRML, PGM); solutions, however, are only accessible to instructors (same with BRML, PGM), which in my experience makes them only half as useful for the self-learner. PRML and ITILA have some solutions online resp. in the book.

- accompanying Matlab/Octave source code, which I found more readily usable than BRML's. PGM and PRML have no accompanying source code, even though the toolkit distributed with Koller's online PGM class might qualify as one. I find accompanying code a truly useful tool for learning; there's nothing like trying to implement an algorithm, checking your implementation against a reference, having boilerplate/utility code for the parts of the algorithm you're not interested in re-implementing. Also, code may clarify an algorithm, even when presented in pseudo-code. By the way, MLAPP has rather few pseudo-code boxes (like BRML or PRML, while PGM is very good here).

- MLAPP is not freely available as a PDF (unlike BRML, closest topic-wise, ESL, or ITILA). This will no doubt reduce its diffusion. My own take on the underlying controversy is in favor of distributing the PDF: makes successful books widely popular and cited (think ITILA or Rasmussen and Williams' Gaussian Processes), increases the book's overall value, equips readers with a weightless copy to annotate with e-ink, or consult on the go. I believe PDF versions positively impact sales, too: impact neutral-to-positive to course textbook/university library sales, indifferent to sales in countries with widely different purchase power, positive to all other segments due to enormous diffusion/popularity.

* Conclusion:
The closest contender to this book I believe is BRML. Both are excellent textbooks and have accompanying source code.

BRML is more accessible, has a free PDF version, and a stronger focus on graphical models.
MLAPP has all the qualities of an excellent graduate textbook (unified presentation, valuable learning aids), and yet is unafraid of discussing detail points (e.g. omnipresent results on complexity), as well as advanced and research topics (LDA, L1 regularization).

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