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This book is a major work that focuses exclusively on ship finance and includes contributions on the increasingly complex field of ship finance, which has over the last two decades become a key aspect in the world of shipping and ship owning. The book offers an enlightening mix of theoretical analysis and well-founded practical insights into the daily markets. Given that ship finance continues to develop dynamically around the world, the book covers subjects ranging from the German KG market to Islamic Finance, from loans to legal aspects and from asset pricing to risk management.

• Sales Rank: #1949264 in Books
• Published on: 2014-11-19
• Original language: English
• Number of items: 1
• Dimensions: 9.21" h x .94" w x 6.14" l, 1.83 pounds
• Binding: Hardcover
• 384 pages

From the Back Cover
This book is a major work that focuses exclusively on ship finance and includes contributions on the increasingly complex field of ship finance, which has over the last two decades become a key aspect in the world of shipping and ship owning. The book offers an enlightening mix of theoretical analysis and well-founded practical insights into the daily markets. Given that ship finance continues to develop dynamically around the world, the book covers subjects ranging from the German KG market to Islamic Finance, from loans to legal aspects and from asset pricing to risk management.

About the Author

About the Editors:

Dr-Ing Orestis Schinas is the Professor of Shipping and Ship Finance and Head of the Maritime Business School of the Hamburg School of Business Administration (HSBA) since 2008. He has served in senior and top-management positions and has a vast experience in the maritime and logistics industry and has offered his services to private and public entities as well as to international organizations. His professional career includes large corporate and business development projects as well as advanced research assignments in the maritime and port sectors. He holds a Dipl-Ing degree in Naval Architecture and Marine Engineering (NTUA, 1994), a Masters in Shipping Management (WMU, 1998) and he was awarded the doctoral title in 2005 (NTUA) for his thesis on Ship Finance. His work is published in major business and academic journals.

Dr. Max Johns is Managing Director at VDR German Ship owners’ Association in Hamburg. He is teaching ship finance at the Maritime Business School of the Hamburg School of Business Administration (HSBA) since 2008. He holds BA and MA degrees from Université Paul Valéry Montpellier III in France, an MBA from KU Leuven in Belgium and was awarded the doctoral title from the University of Tübingen in Germany. He publishes frequently on maritime issues.

Carsten Grau is German lawyer –Rechtsanwalt- and Solicitor (England & Wales). He has practiced maritime and corporate law in Bremen and Hamburg since 1998 and specializes in advising corporate entities and individuals in all areas of maritime business. At HSBA, he is module responsible for the maritime law contents in the MBA Shipping programme. He holds a law degree from the University of Hamburg and a German and Liberian Master Mariner License.

About the Authors:

Sebastian Kewitsch, NAVES Corporate Finance GmbH, http://www.naves-finance.de/

Sebastian Kewitsch studied business economics in Rostock and the MBA shipping at HSBA. He began his career at HSH Nordbank AG where he worked in the areas of real estate, shipping and yards and restructuring. Since 2013 he is working as a Vice President for NAVES Corporate Finance GmbH which provides restructuring, financial asset management, transaction, equity and debt advisory in the maritime industry. The group employs some 30 people in Hamburg. sebastian.kewitsch@naves-finance.com

Vera Alexandropoulou, Attorney at Law, Solicitor, Alexandropoulou Law Firm, alexandropouloulaw.com

Vera Alexandropoulou is a graduate of the Law Faculty of the University of Athens and has completed postgraduate studies at the University of Heidelberg and Harvard University. She was admitted to the Piraeus Bar Association in 1998 and qualified as a UK Solicitor in 2004. She specialises in shipping, energy and environmental law. In 2005 she established her own law firm. She speaks Greek, German, English, French, Spanish and Chinese. vera.alexandropoulou@alexandropouloulaw.com

Nicolas Bornozis, President, Capital Link, Inc., www.capitallink.com

Nicolas Bornozis is the founder of Capital Link, an international advisory, investor relations and financial communications firm with strategic focus on the shipping industry. Capital Link works with the majority of shipping companies listed in European and US Exchanges and has built one of the most extensive and effective platforms for raising awareness of shipping to a wider industry and investor audience. His firm has received industry awards from Lloyds List and Intercontinental Finance Magazine. Nicolas has a Law Degree from the University of Athens and an MBA from Harvard Business School. He has 32 years of experience in the US and European financial and capital markets. nbornozis@capitallink.com

Dr. Christos Sigalas, Seanergy Maritime Holdings Corp., http://www.seanergymaritime.com/ and University of Piraeus, http://www.unipi.gr/

Dr. Christos Sigalas is a professional business planning and corporate finance senior analyst with work experience in maritime shipping, investment banking and telecommunication industries. Dr. Sigalas holds a PhD (2012) with honours from University of Piraeus and an MBA (2005) with honours along with a BSc (2002) in Management & Business Administration from Athens University of Economics and Business. csigalas@seanergymaritime.com.gr or csigalas@webmail.unipi.gr

Andreas Mietzner

Company - Organization: Continental Chartering

Website link: www.continental-chartering.de

Short Bio 2-5 lines

Email research@continental-chartering.de

Andreas Mietzner is shipping analyst at the ship broking firm Continental Chartering. He graduated with a diploma in economics from the Albert-Ludwigs-University Freiburg majoring in psychology and econometrics. He gained further academic experience at the University of Cadiz and University Udayana. He is an expert on shipping markets and provides professional advisory, analysis and vessel valuation services.

Christoph Sturm, Co-Founder Deutsche Seewert GmbH & Co. KG, www.seewert.de and MarLink Schiffahrtskontor GmbH & Co. KG, www.marlink.net

Christoph Sturm has 24 years of practical experience in ship owning, ship managing, chartering and ship financing with assignments in Germany, Asia and the US. He currently focusses on independent interim management and advisory services to the German shipping industry, christoph.sturm@marlink.net or christoph.sturm@aol.com

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This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques – together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts.

The book builds carefully from the basic classical methods  to  the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for  different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.

• All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods.
• The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling.
• Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied.
• MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.

• Sales Rank: #308809 in Books
• Published on: 2015-04-10
• Original language: English
• Number of items: 1
• Dimensions: 2.00" h x 7.60" w x 9.30" l, 5.16 pounds
• Binding: Hardcover
• 1062 pages

Review
"Overall, this text is well organized and full of details suitable for advanced graduate and postgraduate courses, as well as scholars…" --Computing Reviews

From the Back Cover

This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques – together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts.

The book builds carefully from the basic classical methods  to  the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for  different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.

Key Features Include:

• An introductory chapter on related mathematical tools
• All major classical techniques:  Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods
• A presentation of the physical reasoning, mathematical modeling and algorithmic implementation of each method
• The latest trends:  Sparsity, convex analysis and optimization,  online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models,  particle filtering, deep learning, dictionary learning and latent modeling
• Case studies -  protein folding prediction,  optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied
• MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code

About the Author
Sergios Theodoridis is Professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the University of Athens.
He is the co-author of the bestselling book, Pattern Recognition, and the co-author of Introduction to Pattern Recognition: A MATLAB Approach.
He serves as Editor-in-Chief for the IEEE Transactions on Signal Processing, and he is the co-Editor in Chief with Rama Chellapa for the Academic
Press Library in Signal Processing.

He has received a number of awards including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2014 IEEE Signal Processing Society Education Award, the EURASIP 2014 Meritorious Service Award, and he has served as a Distinguished Lecturer for the IEEE Signal Processing Society and the IEEE Circuits and Systems Society. He is a Fellow of EURASIP and a Fellow of IEEE.

Most helpful customer reviews

12 of 13 people found the following review helpful.
The companion MATLAB source codes are also very useful for better understanding of the methods
By S. P. Chatzis
A much needed and long awaited handbook on modern (Statistical) Machine Learning, including the most current trends of Bayesian Non-parametrics and Deep Learning. Very well-written, it helps the students grasp the main concepts as well as the technical details of the method. The companion MATLAB source codes are also very useful for better understanding of the methods. Being an instructor myself, I would rate it as exceptional.

19 of 23 people found the following review helpful.
Good but may be hard to follow
By Andrei
I'm still looking for a "perfect machine learning theory book": the one which is a pleasure to read and that covers most of concepts you see here and there all the time but always wanted to know how exactly they work: log-linear, maximum likelihood, MAP, least squares and MLS, expectation maximization, stochastic gradient descent, CRFs, mixtures of gaussian, and many others. I would like that the book explain to me why should I use this model or algorithm, why previous one would not be good? And I would like that the author take the time to carefully guide the reader throughout the theory, without leaving him alone with a bunch of matrix equations or integrals like if they were evident.

I'm not a novice in the AI: I have a PhD (not in the theoretical Machine Learning though) and several years of practical experience with the algorithms. But most of the time I use the algorithms and models like blackboxes. My goal, however, is not only be able to use the algorithms and know where and how each algorithm can be used, but really understand the math that drives each them.

Unfortunately, this is not the book that can help me with my goal. In the beginning of each chapter the author really tries to move slowly with a care to details, but very fast the math becomes the only language used on the page. If, in the middle of a section you didn't understand how equation 12 follows from equation 11, your only option is to skip the remainder of the section and this is very frustrating.

As an example, when presenting the "central limit theorem", the author writes "Consider N mutually independent random variables, each following its own distribution with mean values ... and variances ... Define a new random variable **as their sum**: ... Then the mean and variance of the new variable are given by...". Here, or before, no definition of a **sum of two random variables** was presented. But this is very important to understand, because later, for example, in the "Linear Regression" section of Chapter 3, the author writes "If we model the system as a linear combiner, the dependence relationship is written as: " (a linear combination of several random variables follows). What does this mean: a linear combination of ***random variables***? How is this related to the central limit theorem which says that by adding up several random variables, the resulting variable tends to have a gaussian distribution? Author, please don't hurry up, it's a book, not a NIPS paper!

Furthermore, the whole section "3.10.1 LINEAR REGRESSION: THE NONWHITE GAUSSIAN NOISE CASE" on page 84 cannot be directly understood from the text because the author does not explain how the joint log-likelihood function L(theta) for the model of y dependent on theta, x and nu can be constructed. The equation 3.57 gives the final expression for L(theta) but no clues on how to build it if we only have a linear model for y. I spent the whole evening just to understand that to build the joint log-likelihood function one has to transform the y = theta*x + nu into the expression p(y=yn | x=xn, theta, nu) and in order to obtain one such expression for each yn, one has to write p(y=yn | x=xn, theta, nu) = sum_k p(xn*theta=k)p(nu=yn - xn*theta). Then, the joint log likelihood L(theta) can be obtained as ln p(y=y1 | x=x1, theta, nu) + ln p(y=y2 | x=x2, theta, nu) + ... + ln p(y=yn | x=xn, theta, nu).

The internet is full of information on the subject of machine learning. Almost every subject is already explained by multiple sources. The problem with the information of the Web is that it is dispersed and often incomplete. If one decides to write a book on this subject, it has to be complete and self-contained. With this book, unfortunately, one still has to google, decrypt and guess things just too often to call the reading process a pleasure.

11 of 12 people found the following review helpful.
It is a great book!
By Paulo S. R. Diniz
It is a great book!!! It covers a wide range of subjects related to machine leaning not found in other books. It is well written and includes detailed reference list in each subject matter. The book should be useful for practitioners, graduate students and academics. I am glad I bought it.

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Featuring 100 years history about Schott NYC which has been manufacturing very cool American iconic leather jackets for bikers, military and punk rockers since 1913!! You can see over 100 rare leather jackets by their Perfecto brand.

• Sales Rank: #1102746 in Books
• Published on: 2012-12-10
• Binding: Hardcover
• 161 pages

About the Author
Rin Tanaka, a Japanese editorial photographer, writer and American men s vintage fashion historian, was born in Japan in 1970. He started hitting U.S. roads to study black music history and play guitar since 1991 during his college days. After working for an advertising company in Tokyo, he moved to San Clemente, California in 1998 to research American vintage fashion and street culture more deeply. Thus far, he has written 21 books about the history of vintage men s clothing, traveling to U.S. cities every month for over 15 years. His books have been published by his self-publishing company and other publishers. In addition, kulture recycle vintage fashion event Inspiration, presented by him, has been held in Los Angeles every 2nd weekend of Feb. since 2010.

Most helpful customer reviews

4 of 4 people found the following review helpful.
Schott -100 Years of an American Original
By Terry Hansen
This is super cool title, featuring about 100 years history of Schott Bros which has been leading LEATHER FASHION brand from NYC since 1913!! Contents are from Pre-WWII leather fashion incling military, rock & Roll leather fashion like Marlon Brando & James Dean, and PUNK fashion too!! Highly recommend to not only leather fashion freaks, but also any vintage American fashion collectors and R&R peoplein the world!!

2 of 2 people found the following review helpful.
A great education
By Keith Stern
An authoritative history of one of the last remaining US craft factories and their beautiful products over the past century.

1 of 1 people found the following review helpful.
Great History
By bl@ster
Great book! Schott has a pretty storied history and Tanaka books are the best. Pick it up.

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• Sales Rank: #4838026 in Books
• Published on: 2012
• Number of items: 1
• Binding: Hardcover

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This is a pre-1923 historical reproduction that was curated for quality. Quality assurance was conducted on each of these books in an attempt to remove books with imperfections introduced by the digitization process. Though we have made best efforts - the books may have occasional errors that do not impede the reading experience. We believe this work is culturally important and have elected to bring the book back into print as part of our continuing commitment to the preservation of printed works worldwide. This text refers to the Bibliobazaar edition.

• Sales Rank: #13971636 in Books
• Published on: 2009-05-18
• Original language: English
• Dimensions: 8.75" h x .87" w x 5.75" l,
• Binding: Paperback
• 382 pages

About the Author
Friedrich Gerhard Rohlfs (1831- 1896) joined the Foreign Legion in Algeria in 1855 before traveling extensively throughout North Africa and the Sahara. He first entered Egypt via Siwa in 1869.

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Why spend time on coding problems that others have already solved when you could be making real progress on your Ruby project? This updated cookbook provides more than 350 recipes for solving common problems, on topics ranging from basic data structures, classes, and objects, to web development, distributed programming, and multithreading.

Revised for Ruby 2.1, each recipe includes a discussion on why and how the solution works. You’ll find recipes suitable for all skill levels, from Ruby newbies to experts who need an occasional reference. With Ruby Cookbook, you’ll not only save time, but keep your brain percolating with new ideas as well.

Recipes cover:

• Data structures including strings, numbers, date and time, arrays, hashes, files and directories
• Using Ruby’s code blocks, also known as closures
• OOP features such as classes, methods, objects, and modules
• XML and HTML, databases and persistence, and graphics and other formats
• Web development with Rails and Sinatra
• Internet services, web services, and distributed programming
• Software testing, debugging, packaging, and distributing
• Multitasking, multithreading, and extending Ruby with other languages

• Sales Rank: #345220 in eBooks
• Published on: 2015-03-24
• Released on: 2015-03-25
• Format: Kindle eBook

About the Author

Lucas Carlson founded AppFog, a PaaS company that leveraged Cloud Foundry and was acquired by CenturyLink. Lucas has been a professional developer for 20 years and specializes in Ruby on Rails development. Lucas has authored Programming for PaaS and the Ruby Cookbook and has written half a dozen libraries in various programming languages and contributed to many others, including Rails and RedCloth.

Leonard Richardson (http://www.crummy.com/) is the author of the Ruby Cookbook (O'Reilly) and of several open source libraries, including Beautiful Soup. A California native, he currently lives in New York.

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0 of 0 people found the following review helpful.
Four Stars
By John Huff
Good Book

1 of 3 people found the following review helpful.
As an ad hoc programmer as part of my test ...
By Lance Taylor
As an ad hoc programmer as part of my test design role, this book has proven invaluable in coming up with the exact right paradigm for writing a function, lamba, or just stealing vast swaths of code in toto.

1 of 3 people found the following review helpful.
Five Stars
By Charles A
Excellent set of 'recipes' for Ruby! Covers Ruby 2.1 which is great.

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Forget everything you've learned about creating new businesses. Forget market research. Forget customer interviews. Forget everything business schools teach. Forget about "finding pains in the market," or gaining user empathy, or validating hypotheses.

Wrong, wrong, wrong.

Without doing any of that, Mark Zuckerberg launched Facebook and had 75% of the Harvard student body join in less than one month; Dropbox wound up with 1 million users and a hockey-stick growth curve seven months after launch; the founder of Spanx went from selling fax machines door-to-door to becoming the world's youngest self-made female billionaire in just over ten years.

But if their success wasn't the result of traditional tactics, how did they do it? Dumb luck? Genius intellect? Magic?

Despite what you were taught in business school and the enticing maxims of the latest "five ways to innovate" articles, the reality is that 50-80% of successful innovations are created by people who simply made something they wanted to use. Then they showed it to other people.

This model accounts for an astounding portion of the world's successful innovations, including the nineteen user method cases:

- Apple

- Facebook

- Ford

- Uber

- GoPro

- Dropbox

- Porsche

- Instagram

- Airbnb

- YouTube

- Spanx

- Qualtrics

- Sunrider

- Patagonia

- Basecamp

- Pebble

Built on hundreds of hours of research and thousands of data points, The User Method tells the astoundingly simple stories of how the founders identified massive opportunities, created phenomenal products, and grew iconically successful businesses, and draws out patterns to form straightforward, replicable principles for innovation and entrepreneurship.

• Sales Rank: #1001342 in eBooks
• Published on: 2015-10-26
• Released on: 2015-10-26
• Format: Kindle eBook

Most helpful customer reviews

5 of 5 people found the following review helpful.
This book is punchy, to the point, at ...
By Amazon Customer
This book is punchy, to the point, at times delightfully personal, and full of page-turning mini-cases from well-known and successful innovators. This book is well worth the inspiration and ideas it imparts. I plan to use it in the innovation and entrepreneurship courses I teach.

3 of 3 people found the following review helpful.
This is what people should be talking about in the startup world.
By Amazon Customer
WELL DONE! This is what people should be talking about in the startup world.

I loved this book. It got better and better. I was excited to see what insights I could draw next. The stories drew me in and I felt like I was really learning form the experiences of famous founders at companies like GoPro, DropBox, Spanx, Uber, and surprisingly Ford even. I was totally engrossed. It was an experience.

So grateful this book used so many cool examples and didn't just dive into meaningless discussion about stupid meta ideas like Innovation Accounting (I hate The Lean Start Up, such a dumb book).

Founders used to seem like untouchable magical visionaries who saw the future. But it makes more sense that they searched for something they wanted, like a camera to take surfing. Then finally decided to make one for themselves after they couldn't find it anywhere. From the get-go these founders were experts on their products. They knew from personal experience what these products needed to be.

And it is so much simpler. You don't have to go out and interview people to find their "pain points" (barf). Steve Jobs said it himself. People don't know what they want when you ask them.

You make a product you really want because you can't find it anywhere. You make it good because why would you want to use something crappy. And then at the least you made your life better. And chances are so high that other people will see what you made and want it too!

The rock climbing company guy making himself better equipment! Sooooo cool. Steve Wozniak wanting a personal computer his whole life. Ferry Porsche wanting a light weight car that handled well. This book is all I want to talk about!

oh! and P.S. I loved the personal stories. You have some guts meeting dudes in parking lots to make planners and you were so real when describing your struggles as an entrepreneur. You've got moxie. I hope your ideas make it somewhere. We have too many kids coming out of business school starry eyed trying to be the next big thing without any substance.

Maybe they all need to just get jobs until they really find a big idea.

5 STARS!!! Good show. This is what people should be talking about in the startup world.

3 of 3 people found the following review helpful.
Every aspiring entrepreneur should read this
By A Customer
It seems like just about everyone wants to be an entrepreneur, which is wonderful, but most startups fail. Schwarting reveals a startlingly simple idea that explains why most startups fail but some grow like crazy. This simple idea is called the User Method.

Schwarting relates many stories of failed startups and the problem is, basically, always the same: startups fail because they don't offer a (useful) solution to an intense need; not enough people really care about using the startup's product. As the CEO of a software startup and alumni of one of the world's top accelerators, I've experienced this startup failure firsthand. If I had known about the User Method two years ago, it may have saved me a lot of heartache because it sheds light on how some of the biggest companies you've ever heard of (Apple, YouTube, Air BNB, Uber, GoPro, etc.) all started the same way: the founders built solutions to THEIR OWN problems for THEMSELVES.

Schwarting's research and insights are invaluable to aspiring entrepreneurs, innovators, and creators. To borrow the cliché, the User Method truly is a "must read."

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