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Free Ebook , by P. W. Singer

Free Ebook , by P. W. Singer

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, by P. W. Singer

, by P. W. Singer


, by P. W. Singer


Free Ebook , by P. W. Singer

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, by P. W. Singer

Product details

File Size: 2281 KB

Print Length: 364 pages

Publisher: Cornell University Press; 2 edition (November 29, 2007)

Publication Date: November 29, 2007

Sold by: Amazon Digital Services LLC

Language: English

ASIN: B0056BX7AS

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Amazon Best Sellers Rank:

#276,983 Paid in Kindle Store (See Top 100 Paid in Kindle Store)

P.W. Singer's Corporate Warriors is an excellent book about the world of private military firms (also called private military security contractors).When he wrote this, the United States was not yet involved in Iraq, let alone against ISIS. It was updated in 2008 as Barack Obama was running for president so it has been somewhat updated though not completely. It could definitely use another given the events of the past eight years.Nonetheless, it is an invaluable introduction to the subject (there are several newer books available now), a subject about which I knew very little going in. It presents a disturbing picture of the world in which private military firms are largely unregulated by both national and international law, presenting all sorts of potential difficulties. We have already seen some of these in the Abu Ghraib scandal and in the behavior of Blackwater during the Iraq War and subsequent occupation and civil war.Singer goes into great detail regarding the history and development of these contractors and the various uses for which they have been hired by our own and foreign governments. There is an example order of battle and a sample contract, both of which are very illuminating. It is heavily footnoted and you can tell Mr. Singer, who is known also for his books about robots and warfare and cybersecurity, did his homework.Highly recommended for anyone interested in the subject or in national security and foreign policy.

Mr Singer provides an excellent study of a millenia-old dynamic: the use of mercenaries in warfare. Of course, today's mercs are referred to in much different, politically-correct terms - but that doesn't change the basic principle on which these warrior-for-hire are founded. It is important, though, to understand the changes in the days even since Executive Outcomes, and the evolution that has taken place since then. This work, along with several other written in recent years, do a functional service to the global public by helping to educate those unfamiliar with this often misunderstood profession, its corporte backers, and the entities that hire them today. In my opinion, there is definitely a role for private military companies (call them what you will) in this modern world, and there is little question that - just as they were used 2,000 years ago - the mercenary will always have a role to play in international conflicts. Having attended some training at Blackwater and met Eric Prince (following 9/11, prior to Blackwater becoming a rent-a-cop's dream vacation), and having worked in the national security field, this subject is of intense interest to me, personally. This work furthered my knowledge on the subject and I think should be read by everyone who has an interest in national and international security.

In Corporate Warriors, Singer provides historical background for the hiring of private military personnel and then describes their contemporary use globally. He notes that the employment of mercenary soldiers began thousands of years ago. But as modern states developed, governments moved to place all military forces under their control. Reliance on hired soldiers declined significantly by the beginning of the twentieth century. During the Cold War, U.S. and Soviet forces provided military assistance to the armies of developing nations reducing the inclination of those countries to hire mercenaries. Singer explains that changing economic and political conditions fostered the rise of modern private military companies. He notes that the market place has ended the state’s monopoly over violence and that private military companies are now active all over the world. The hiring of private military companies appears to have increased for a number of reasons. The reduction of the U.S. armed forces after the end of the Cold War led to a renewed market for private military contractors. It may be cheaper for small or weak states to hire private military companies when they perceive the need for force rather than maintaining sizeable standing armies. Certain corporations and humanitarian groups may hire private military companies for protection in dangerous areas where local police are too few, too corrupt or too incompetent. Demobilization of armed forces after the end of the Cold War increased the availability of weapons and trained military personnel in search of new jobs. Singer also describes how the 2003 Iraq War provided new opportunities for private military companies. This book is a fascinating and enlightening examination of an internationally important topic..

P.W. Singer has presented a real world portrayal of the way military operations are trending. There is a much deeper story to be had in the operations that a supported as private companies that are true government operations that hide in plain view. Companies started, ran, and operated by the US government but stand opening as supposed private companies. I am not, in any way, saying that is bad. It provides total control over a situation by controlling all facets of the operation. The story is much more interesting but if truly told would hobble their efforts. A story that would stand so firmly as fiction that the truth could be boldly told and believed by no one. The public would find it absurd that such an operation could go unnoticed standing so openly for all to see. A job well done here in the book and to those that operate secretly in plain view. I enjoyed it... the book and ...

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Download PDF Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Connected to this Feature Engineering For Machine Learning: Principles And Techniques For Data Scientists, you can get it right here straight. This publication is among the collections in this on-line collection to check out quickly. With the sophisticated modern technology, we will certainly reveal you why this publication is referred. It is kind of completely upgraded book with great headline of the message and also instances. Some workout as well as applications exist that will certainly make you really feel extra innovative. Connected to this situation, this publication is used to earn the right selection of analysis products.

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists


Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists


Download PDF Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

About the Author

Alice is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. She received a Ph.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley.

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Product details

Paperback: 218 pages

Publisher: O'Reilly Media; 1 edition (April 14, 2018)

Language: English

ISBN-10: 1491953241

ISBN-13: 978-1491953242

Product Dimensions:

7 x 0.4 x 9.1 inches

Shipping Weight: 12.5 ounces (View shipping rates and policies)

Average Customer Review:

4.1 out of 5 stars

8 customer reviews

Amazon Best Sellers Rank:

#114,128 in Books (See Top 100 in Books)

It's great that someone's written a book about Feature Engineering and it contains a lot of interesting material. I also like that clear readable coding examples are included (Python), so that the reader can try at home.But, it feels unfinished. The book is pretty short and the set of topics seems more like what the authors were interested in than a survey of the field. The level of explanation varies from super-detailed to glossing over concepts and technical terms that most readers won't know.The biggest issue is that the graphics are mostly hand-drawn on some kind of tablet and look like they were scrawled out quickly. I've attached one example. As you can see, they didn't even bother to adjust the contrast. So, the images have a dull gray background. Disappointing that O'Reilly didn't take care of this for the authors.

A good reference book for those of us in the daily engagement of machine learning.

Good intro to the feature engineering with clear examples in Python. Overall 5 stars as the book is easy to read and it contains useful hints.

Well written. Highly recommended.

This book does a great job explaining the "why".

At the end of the preview, the book recommends converting song listening counts into a binary 0 1 variable. This is TERRIBLE advice. It mentions binning as an alternative but goes ahead with binary code. The authors argument is that "listening 10 times does not mean liking the song as much as someone who listens ,,20 times."In other words, if the user listened to a song at least once, then we count it as the user liking the song. This way, the model will not need to spend cycles on predicting the minute differences between the raw counts. The binary target is a simple and robust measure of user preference.". There are many ways to deal with possible non-proportional effects. Going to a 1 treats someone who tried a song and did not like it the same as someone who listens daily!I would love a good book on this topic for my students, but this is not it..

I have read almost half of the book. It is amazingly useful, concrete and helpful. I can't stop finishing the book and then write my review and I would like to appreciate authors for their great work.Based on my experience the process of machine learning is 80% data wrangling, cleaning, feature engineering,... and 20% running the model or ML algorithm. This book aims for the first 80%.

I’ve read the pre-release version on safari’s books. Excellently written and great examples. I thoroughly enjoyed the initial chapters on transforming input data.

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