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To Catch a Spy: The Art of Counterintelligence by James M. Olson
- To Catch a Spy: The Art of Counterintelligence
- James M. Olson
- Page: 248
- Format: pdf, ePub, mobi, fb2
- ISBN: 9781626166806
- Publisher: Georgetown University Press
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The United States is losing the counterintelligence war. Foreign intelligence services, particularly those of China, Russia, and Cuba, are recruiting spies in our midst and stealing our secrets and cutting-edge technologies. In To Catch a Spy: The Art of Counterintelligence, James M. Olson, former chief of CIA counterintelligence, offers a wake-up call for the American public and also a guide for how our country can do a better job of protecting its national security and trade secrets. Olson takes the reader into the arcane world of counterintelligence as he lived it during his thirty-year career in the CIA. After an overview of what the Chinese, Russian, and Cuban spy services are doing to the United States, Olson explains the nitty-gritty of the principles and methods of counterintelligence. Readers will learn about specific aspects of counterintelligence such as running double-agent operations and surveillance. The book also analyzes twelve actual case studies to illustrate why people spy against their country, the tradecraft of counterintelligence, and where counterintelligence breaks down or succeeds. A “lessons learned” section follows each case study.
By James M. Olson - Fair Play: The Moral Dilemmas of Spying: 1st
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To Catch a Spy book. Read reviews from world's largest community for readers. The United States is losing the counterintelligence war. Foreign intelligen Thwarting Enemies at Home and Abroad: How to Be a
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Spy VS. Spy [Kessler] on Amazon.com. *FREE* shipping on qualifying offers. Spy Hardcover – June 2, 1988 . a new TV series, were it not for Kessler's sobering comments on the tricky problem of keeping our freedoms while catching their spies. . Books, art & collectibles · ACX Audiobook Publishing Made Easy · Alexa To Catch a Spy – HFS Books
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Salmon: A Fish, the Earth, and the History of Their Common Fate. Mark Kurlansky
Salmon-A-Fish-the-Earth.pdf
ISBN: 9781938340864 | 416 pages | 11 Mb
- Salmon: A Fish, the Earth, and the History of Their Common Fate
- Mark Kurlansky
- Page: 416
- Format: pdf, ePub, fb2, mobi
- ISBN: 9781938340864
- Publisher: Patagonia
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Ebook download for free Salmon: A Fish, the Earth, and the History of Their Common Fate 9781938340864 FB2 RTF MOBI by Mark Kurlansky
Overview
In what he says is the most important piece of environmental writing in his long and award-winning career, Mark Kurlansky, best-selling author of Salt and Cod, The Big Oyster, 1968, and Milk, among many others, employs his signature multi-century storytelling and compelling attention to detail to chronicle the harrowing yet awe-inspiring life cycle of salmon. During his research Kurlansky traveled widely and observed salmon and those who both pursue and protect them in the Pacific and the Atlantic, in Ireland, Norway, Iceland, Japan, and even the robust but not as frequently visited Kamchatka Peninsula. This world tour reveals an eras-long history of man’s misdirected attempts to manipulate salmon and its environments for his own benefit and gain, whether for entertainment or to harvest food. In addition, Kurlansky’s research shows that all over the world these fish, uniquely connected to both marine and terrestrial ecology as well as fresh and salt water, are a natural barometer for the health of the planet. He documents that for centuries man’s greatest assaults on nature, from overfishing to dams, from hatcheries to fish farms, from industrial pollution to the ravages of climate change, are evidenced in the sensitive life cycle of salmon. With stunning historical and contemporary photographs and illustrations throughout, Kurlansky’s insightful conclusion is that the only way to save salmon is to save the planet and, at the same time, the only way to save the planet is to save the mighty, heroic salmon.
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Kingdoms of Faith: A New History of Islamic Spain by Brian A. Catlos
- Kingdoms of Faith: A New History of Islamic Spain
- Brian A. Catlos
- Page: 496
- Format: pdf, ePub, mobi, fb2
- ISBN: 9780465055876
- Publisher: Basic Books
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Naomiki Sato's Origami Roses: Create Lifelike Roses and Other Blossoms by Naomiki Sato
- Naomiki Sato's Origami Roses: Create Lifelike Roses and Other Blossoms
- Naomiki Sato
- Page: 108
- Format: pdf, ePub, mobi, fb2
- ISBN: 9784805315200
- Publisher: Tuttle Publishing
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Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps. Valliappa Lakshmanan, Sara Robinson, Michael Munn
Machine-Learning-Design.pdf
ISBN: 9781098115784 | 400 pages | 10 Mb
- Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
- Valliappa Lakshmanan, Sara Robinson, Michael Munn
- Page: 400
- Format: pdf, ePub, fb2, mobi
- ISBN: 9781098115784
- Publisher: O'Reilly Media, Incorporated
Books pdf download Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps in English 9781098115784
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow. The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the most appropriate remedy for your situation. You’ll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure that models are treating users fairly
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