Senin, 05 Januari 2015

!! Ebook So Bad, It's Good: More Than 50 Great Films for Your Bad Movie Night, by Edward Scimia

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So Bad, It's Good: More Than 50 Great Films for Your Bad Movie Night, by Edward Scimia

Citizen Kane. Casablanca. Troll 2. Does one of these titles feel as though it doesn't belong with the other two as a monument of cinematic achievement? Or have you developed the sophisticated taste and keen eye that allows you to appreciate when a movie really is so bad, it's good, giving you the confidence and conviction to assert why Troll 2 truly is a movie for the ages? These questions are at the heart of So Bad, It's Good, Edward Scimia's fun and informative new guide to the ever-expanding catalogue of egregiously flawed, yet oddly endearing, B-movies.

Watching bad movies can be surprisingly fun, if you choose the right films. In fact, after reading the profiles of more than fifty movies included in the book, you might just decide to have a "bad movie night" of your own. For those who love the barbed humor and wry perspective of shows like Mystery Science Theater 3000, So Bad, It's Good is an indispensable compendium that will thrill those intrepid film buffs who like to laugh at the worst cinema has to offer. Spanning decades and genres, So Bad, It's Good naturally explores inadvertent classics, such as Ed Wood's Plan 9 From Outer Space, while equally gleaning more recent selections.

Writing with an entertaining and engaging flair and drawing on a profound personal affection for bad movies, Edward Scimia lovingly pokes fun at these milestones of misguided cinematic ambition, while also documenting the sincere and earnest efforts that went into their creation. The result is a comprehensively researched and carefully curated guide to the best of the worst in movies full of ludicrous scenarios, implausible plot twists, and abysmally awkward dialogue. So Bad, It's Good also features a foreword by Allison Pregler of the web series "Obscurus Lupa Presents."

  • Sales Rank: #510211 in eBooks
  • Published on: 2012-12-10
  • Released on: 2012-12-10
  • Format: Kindle eBook

About the Author
Edward Scimia is a freelance writer based in Bethel, Connecticut. Having graduated from Syracuse University in 2003 with degrees in magazine journalism and political science, he presently writes online content for a variety of clients and also serves as the Chess Guide for About.com. He is the author of the Real Mysteries series of books and multiple books on poker strategy, including Catching Fish. His hobbies include playing chess, spending quality time with his cat, Zoey, and, in the winter, curling.

Most helpful customer reviews

8 of 9 people found the following review helpful.
When Bad Apples Become Delicious Oranges
By Michael G. Knight
Some bad movies cross a nebulous threshold and enter a dimension where the sub-par becomes sublime. It is a difficult territory to define; to paraphrase Justice Stewart Potter, I know it after I see it. This subjective quality can not be made into a film and is only revealed upon viewing(s). Sadly, most bad movies are, to paraphrase Joe Estevez in Werewolf, bad bad. Accordingly, there are a precious few willing to sacrifice their sanity in charting this morass of incompetence. Thankfully, Edward Scimia has returned from his journey, relatively unscathed, and given us this treasure map detailing where to find diamonds in Hollywood's (or Turkey's) dung heap.

Succinct (143 pgs) and smart ("If you liked Dolph Lundgren as a deadly Russian boxer, you'll love him as a well-rounded East German athlete!), So Bad, It's Good, is a splendid little "good bad" movie guide. The reader will find concise plot summaries and production notes/trivia on most films. In his synopses, Scimia criticizes the often ridiculous plot points without sounding pedantic or condescending; one senses the author's earnest appreciation for these films.

If you are a connoisseur of the so-bad-it's-good canon, you are familiar with Troll 2, The Room and many of the films shown to Joel/Mike and his bots, but have you seen the two animated Titanic pictures described inside this book? If you have, I sympathize with you.

4 of 4 people found the following review helpful.
Manos? Check. Hobgoblins? Check. Vanilla Ice? Check.
By Robert I. Hedges
Edward Scimia has written a brilliant book on bad movies and the people who love them. I appreciated his approach to bad movies right away when he explained as part of his theory of cult movie greatness that "A truly bad movie can't be forced. It has to be the result of a director's legitimate attempt to make a good film and coming up short." I couldn't agree more. While there are, on occasions, excellent spoofs and satires (e.g. "The Lost Skeleton of Cadavra,") most intentionally or deliberately bad movies aren't enjoyable, especially those that try to be funny (which almost never are, e.g. "Chillerama.") While I wish Scimia's imagined "Ninja Koala" and "Chainsaw Feet" were real movies, he does present 50 compelling choices in general categories for your late night entertainment.

While everyone will have their favorite good bad movies, few would deny that "Troll 2," "Manos: The Hands of Fate," "Hobgoblins," or anything by Ed Wood are in the rarified ether of cult movie greatness. I like that Scimia covers those topics in interesting detail (the background provided of "Troll 2" is especially excellent) and pays homage to MST3K as the gateway drug to cult movie fandom (I couldn't agree more, though I disagree strongly that "The Final Sacrifice" is one of the greats.) That's all low-hanging fruit, and I was even more impressed with the more obscure movies Scimia came up with (and I'm speaking as someone who has seen "Pocket Ninjas.") That he could integrate such diverse works as "Cool As Ice" with Vanilla Ice, not one, but two Italian animated "Titanic" movies, Turkish films from the 1980s, the Asylum "mockbusters" ("Mega Piranha," etc.,) and the unfathomable big budget "Tiptoes" into one book is testament to both his prowess as a writer and his dedication to B-movies.

I absolutely loved this book: it's fun and it's genuinely informative. I you are a fan of MST3K, bad movies, or cinematic aberrations, "So Bad, It's Good" is a great book for you.

2 of 2 people found the following review helpful.
A fantastic voyage...
By Margaret B
...into the world of B movies!

Have you ever seen a movie that was awful, but for some reason you came back to watch it again and again? Well, this book is stuffed full of other movies JUST LIKE THAT! Expand your repertoire of cheesy, over-the-top or sometimes just painful films with this handy guide covering films from many decades and genera of cinematic "master"pieces.

It's a quick read, fair-handed in its description of movies and maintains a light tone without disparaging the oftentimes sincere and hardworking writers, producers, cast and crew of the films it reviews. I'm looking forward to added editions to this book!

See all 17 customer reviews...

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  • Sales Rank: #75712 in eBooks
  • Published on: 2012-12-23
  • Released on: 2012-12-23
  • Format: Kindle eBook

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Sabtu, 03 Januari 2015

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R and Data Mining: Examples and Case Studies, by Yanchang Zhao

This book guides R users into data mining and helps data miners who use R
in their work. It provides a how-to method using R for data mining
applications from academia to industry.
It

  • Presents an introduction into using R for data mining applications, covering most popular data mining techniques
  • Provides code examples and data so that readers can easily learn the techniques
  • Features case studies in real-world applications to help readers apply the techniques in their work and studies
The R code and data for the book are provided at the RDataMining.com website.

The
book  helps researchers in the field of data mining, postgraduate
students who are interested in data mining, and data miners and analysts
from industry. For the many universities that have courses on data
mining, this book is an invaluable reference for students studying data
mining and its related subjects. In addition, it is a useful resource
for anyone involved in industrial training courses on data mining and
analytics. The concepts in this book help readers as R becomes
increasingly popular for data mining applications.

  • Sales Rank: #1293184 in eBooks
  • Published on: 2012-12-31
  • Released on: 2012-12-31
  • Format: Kindle eBook

From the Author
Table of Contents:

1 Introduction
    1.1 Data Mining
    1.2 R
    1.3 Datasets
        1.3.1 The Iris Dataset
        1.3.2 The Bodyfat Dataset

2 Data Import and Export
    2.1 Save and Load R Data
    2.2 Import from and Export to .CSV Files
    2.3 Import Data from SAS
    2.4 Import/Export via ODBC
        2.4.1 Read from Databases
        2.4.2 Output to and Input from EXCEL Files

3 Data Exploration
    3.1 Have a Look at Data
    3.2 Explore Individual Variables
    3.3 Explore Multiple Variables
    3.4 More Explorations
    3.5 Save Charts into Files

4 Decision Trees and Random Forest
    4.1 Decision Trees with Package party
    4.2 Decision Trees with Package rpart
    4.3 Random Forest

5 Regression
    5.1 Linear Regression
    5.2 Logistic Regression
    5.3 Generalized Linear Regression
    5.4 Non-linear Regression

6 Clustering
    6.1 The k-Means Clustering
    6.2 The k-Medoids Clustering
    6.3 Hierarchical Clustering
    6.4 Density-based Clustering

7 Outlier Detection
    7.1 Univariate Outlier Detection
    7.2 Outlier Detection with LOF
    7.3 Outlier Detection by Clustering
    7.4 Outlier Detection from Time Series
    7.5 Discussions

8 Time Series Analysis and Mining
    8.1 Time Series Data in R
    8.2 Time Series Decomposition
    8.3 Time Series Forecasting
    8.4 Time Series Clustering
        8.4.1 Dynamic Time Warping
        8.4.2 Synthetic Control Chart Time Series Data
        8.4.3 Hierarchical Clustering with Euclidean Distance
        8.4.4 Hierarchical Clustering with DTW Distance
    8.5 Time Series Classification
        8.5.1 Classification with Original Data
        8.5.2 Classification with Extracted Features
        8.5.3 k-NN Classification
    8.6 Discussions
    8.7 Further Readings

9 Association Rules
    9.1 Basics of Association Rules
    9.2 The Titanic Dataset
    9.3 Association Rule Mining
    9.4 Removing Redundancy
    9.5 Interpreting Rules
    9.6 Visualizing Association Rules
    9.7 Discussions and Further Readings

10 Text Mining
    10.1 Retrieving Text from Twitter
    10.2 Transforming Text
    10.3 Stemming Words
    10.4 Building a Term-Document Matrix
    10.5 Frequent Terms and Associations
    10.6 Word Cloud
    10.7 Clustering Words
    10.8 Clustering Tweets
        10.8.1 Clustering Tweets with the k-means Algorithm
        10.8.2 Clustering Tweets with the k-medoids Algorithm
    10.9 Packages, Further Readings and Discussions

11 Social Network Analysis
    11.1 Network of Terms
    11.2 Network of Tweets
    11.3 Two-Mode Network
    11.4 Discussions and Further Readings

12 Case Study I: Analysis and Forecasting of House Price Indices
    12.1 Importing HPI Data
    12.2 Exploration of HPI Data
    12.3 Trend and Seasonal Components of HPI
    12.4 HPI Forecasting
    12.5 The Estimated Price of a Property
    12.6 Discussion

13 Case Study II: Customer Response Prediction and Profit Optimization
    13.1 Introduction
    13.2 The Data of KDD Cup 1998
    13.3 Data Exploration
    13.4 Training Decision Trees
    13.5 Model Evaluation
    13.6 Selecting the Best Tree
    13.7 Scoring
    13.8 Discussions and Conclusions

14 Case Study III: Predictive Modeling of Big Data with Limited Memory
    14.1 Introduction
    14.2 Methodology
    14.3 Data and Variables
    14.4 Random Forest
    14.5 Memory Issue
    14.6 Train Models on Sample Data
    14.7 Build Models with Selected Variables
    14.8 Scoring
    14.9 Print Rules
        14.9.1 Print Rules in Text
        14.9.2 Print Rules for Scoring with SAS
    14.10 Conclusions and Discussion

15 Online Resources
    15.1 R Reference Cards
    15.2 R
    15.3 Data Mining
    15.4 Data Mining with R
    15.5 Classification/Prediction with R
    15.6 Time Series Analysis with R
    15.7 Association Rule Mining with R
    15.8 Spatial Data Analysis with R
    15.9 Text Mining with R
    15.10 Social Network Analysis with R
    15.11 Data Cleansing and Transformation with R
    15.12 Big Data and Parallel Computing with R

About the Author
Dr. Yanchang Zhao is a Senior Data Mining Specialist in Australian public sector. Before joining public sector, he was an Australian Postdoctoral Fellow (Industry) at University of Technology, Sydney from 2007 to 2009. He is the founder of the RDataMining.com website and an RDataMining Group on LinkedIn. He has rich experience in R and data mining. He started his research on data mining since 2001 and has been applying data mining in real-world business applications since 2006. He has over 50 publications on data mining research and applications, including three books. He is a senior member of IEEE, and has been a Program Chair of the Australasian Data Mining Conference (AusDM 2012 & 2013) and a program committee member for more than 50 academic conferences.

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13 of 13 people found the following review helpful.
Low-quality and savagely overpriced
By Dimitri Shvorob
It's not all bad - I really like the R-resources links in Chapter 15, and give points for Chapters 10 and 11, with basic examples of text mining and network analysis, and for the predictive-modeling case study in Chapter 13. (But why do the percentages on page 172 exceed 100?) However, "R and data mining" is not worth anywhere near $70, and as far as substance and quality are concerned, it is one of the weakest books I have seen. On one hand, you are introduced to several useful built-in R functions and "add-on" R packages, including "party" for classification trees, "cluster" and "fpc" for clustering, "arules" for association-rule learning, "tm" for text mining and "igraph" for network visualization. On the other hand, until Chapter 15, there is pretty little value-added - it's as if the author googled a package, and copy-pasted a vignette from the doc. Things are really basic throughout, even where one might expect complexity - Chapter 14 has the most disappointing example. The page count (200+) overstates content, as the book is seriously heavy on whitespace: code and output, hideously typeset, takes up way more space than needed and is often redundant. I do not recommend the purchase, and suggest "Machine learning with R" by Brett Lantz as a better alternative.

UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.

7 of 7 people found the following review helpful.
Pricey and data unavailable
By chrismatic
The book is way too pricey for its content and some data in the examples are not even available publicly and need to be purchased separately

5 of 6 people found the following review helpful.
Not worth buying
By Graham Webster
I have only read a draft copy that the author has / had on his website, and it is a very disappointing book. For example, the content about each data mining method is very sparse, and as one other reviewer noted, with lots of white space, code, and output. Very little comment about how to use the methods in practice. It certainly looks as though for these chapters the author has copy / pasted material from R package documentation. Not worth buying, there is a lot of other material available of much better quality.

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Jumat, 02 Januari 2015

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  • Published on: 2012-08-31
  • Released on: 2011-06-04
  • Original language: English
  • Dimensions: 10.00" h x .57" w x 7.50" l,
  • Binding: Paperback
  • 250 pages

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  • Published on: 1958
  • Binding: Paperback

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  • Sales Rank: #120869 in Books
  • Published on: 2013
  • Binding: Printed Access Code

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By lisa l watson
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  • Published on: 2012-08-31
  • Released on: 2011-06-04
  • Original language: English
  • Dimensions: 10.00" h x 1.02" w x 7.50" l,
  • Binding: Paperback
  • 450 pages

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