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Senin, 30 April 2018

Download , by Carl F. Luckey

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, by Carl F. Luckey

Product details

File Size: 33759 KB

Print Length: 770 pages

Publisher: Krause Publications; 8 edition (November 9, 2010)

Publication Date: November 9, 2010

Sold by: Amazon Digital Services LLC

Language: English

ASIN: B004EEOEL4

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Amazon Best Sellers Rank:

#296,733 Paid in Kindle Store (See Top 100 Paid in Kindle Store)

I have several past editions of this guide and find it invaluable for identifying lures. I have identified unknown lures for Russ Lewis for his 6 book series and in the 6th edition of this guide identified an unknown lure... the Ping-a-tee...while it was still in production!!I don't rely on any of these guides for actual but only relative pricing value since condition is a very critical factor in the value of all fishing tackle. This guide along with the 6 volume set by Russell Lewis of more modern fishing tackle really covers anything you will probably find in any tackle box !

I already owned the sixth edition of this book, but purchased the eight edition in hopes that more information would be presented on some of the more obscure lure manufacturers. In some instances, this is the case, however, it seems that the author abbreviated much of the information on the more popular manufacturers in order to save space for the new companies added to the eighth edition. In many cases, I find myself still relying heavily on the older edition. Using both books seems to be the best solution.

This 670-page book contains much more than just descriptions of lures. I got the 5th Edition(1999) which serves me, a novice, very well. It contains lure descriptions, tips on collecting,and an expansive list of lures. I'm certain it will be a valuable reference as I move forward. The cost was a small fraction of the latest edition. I've already found information on a couple of lures that folks on Ebay had listed as "manufacturer unknown.

my husband received an old tackle box full of lures from a friend who just did not want it any more. we knew there were some from the early 20's to 40's,other than that we knew nothing about them. we found several books we thought might help us. we had a tough time deciding between this one and several others on amazon. at the last min we decided to get this one and are we glad. has wonderful history, easy to use index. it is a wealth of information. we have identified dozens of tackle. this book is HIGHLY recommended if you have alot of old lures.

would like more info on values

Husband has been looking for just this type of a "encyclopedia" and he is thrilled.

A great book--an unbelievable amount of pictures and information. You'd have to search the internet for hours, maybe days, to find the pictures and descriptions contained here. Very impressive.

good book

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Selasa, 17 April 2018

PDF Download Bird at the Buzzer: UConn, Notre Dame, and a Women's Basketball Classic, by Jeff Goldberg

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Bird at the Buzzer: UConn, Notre Dame, and a Women's Basketball Classic, by Jeff Goldberg

Bird at the Buzzer: UConn, Notre Dame, and a Women's Basketball Classic, by Jeff Goldberg


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Bird at the Buzzer: UConn, Notre Dame, and a Women's Basketball Classic, by Jeff Goldberg

Review

“This is the definitive account of a landmark game told by someone with unique access and insight. If you love UConn women’s basketball, you’ll love this book.”—Rebecca Lobo, UConn ’95 player, ESPN women’s basketball analyst, and 2010 Women’s Basketball Hall of Fame inductee (Rebecca Lobo 2010-10-04)“A well-written account of a seminal moment in an expanding sport by an intelligent, incisive observer.”—Wes Lukowsky, Booklist (Wes Lukowsky Booklist Online 2011-02-15)

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About the Author

Jeff Goldberg covered the 2001 Big East Tournament final for the Chicago Tribune and was the Hartford Courant’s UConn women’s beat writer from 2001 to 2006. He is currently the Red Sox beat writer for Metro Boston. Doris Burke is a television reporter who covers both men’s and women’s college and pro games for ESPN and ABC Sports.

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Product details

Hardcover: 320 pages

Publisher: University of Nebraska Press; First Edition edition (March 1, 2011)

Language: English

ISBN-10: 0803224117

ISBN-13: 978-0803224117

Product Dimensions:

5.8 x 1 x 8.8 inches

Shipping Weight: 1 pounds (View shipping rates and policies)

Average Customer Review:

4.0 out of 5 stars

14 customer reviews

Amazon Best Sellers Rank:

#1,640,684 in Books (See Top 100 in Books)

Goldberg does his usual exceptional job of catching the real stuff and painting it intriguingly. Even NON-UCONN Fans will find his treatment fair and yet rabid UConn fans will find all the rich memories they cherish carefully preserved. Worth the time; worth the money. Your kids will thank you; the girls for your thoughtful compendium of actual sports history; the boys for the education they will get about how real sports "used to be"!

As a newcomer to women's basketball, and of course to UCONN, I loved being immersed in Goldberg's description of what was described as an "instant classic." Usually it is only a good mystery that can be described as hard to put down, but I loved reading this book and did it all too quickly. I felt a part of the team, the game and the time. It was a great read!

This was a classic book. Although it ostensibly was to cover one great game, the real subject was the emergence of women's college basketball as a relevant sport in the U S.It also analyzed the incredible UConn phenomena, and the part that played in drawing fans to the game.Incredibly well researched and well written.

Best blow by blow description of a basketball game I ever read.

Love Uconn WBB and all the stories that surround the program. Sue Bird is one of the greatest.

Another good read on UConn women's basketball. I love and respect the girls even more! This book was also in great condition as described.

Awesome read for UCONN WBB fan!

I just got this book today and I haven't put it down. This book does the game and the season justice. Goldberg does an excellent job telling an amazing story. I have not finished this book and I will provide a more comprehensive review later but if you love basketball you'll love this book. Rebecca Lobo is right, if you love UConn basketball, you'll love this book.

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Kamis, 05 April 2018

Ebook Download The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, by Pedro Domingos

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About the Author

Pedro Domingos is a professor of computer science at the University of Washington. He is a winner of the SIGKDD Innovation Award, the highest honor in data science. A fellow of the Association for the Advancement of Artificial Intelligence, he lives near Seattle.

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Product details

MP3 CD

Publisher: Brilliance Audio; Unabridged edition (January 24, 2017)

Language: English

ISBN-10: 1501299387

ISBN-13: 978-1501299384

Product Dimensions:

5.2 x 0.5 x 6.8 inches

Shipping Weight: 2.4 ounces (View shipping rates and policies)

Average Customer Review:

4.2 out of 5 stars

184 customer reviews

Amazon Best Sellers Rank:

#453,127 in Books (See Top 100 in Books)

This book isn't perfect, but I'm still giving it 5 stars because it provides a better overview of the entire field of Machine Learning than any other book I've come across. My background is in computer science and software engineering and I've been interested ML as more of a hobbyist and outside observer for a few years (reading some books, taking Andrew NG's coursera course), just recently dabbling in some applications professionally. What I was still missing before reading this book was as high a level understanding of where all of the models and technique in the field of ML fit. Other books describe the difference between supervised and unsupervised learning, but this book goes further in describing how, say, decisions trees, support vector machines and deep neural networks fit compared to each other and within which subfields statistics play a larger role than others.The book also puts many techniques in historical perspective that I found very helpful, such as the rise, fall and rise again of deep neural networks with support vector machines taking a lead as the hottest technique in between (while also making clear that SVMs are a useful technique with unique strengths today). Finally, it makes clear that these techniques are not all competing for being the best overall at everything, but that they can be used quite complementary and/or they have unique strengths within certain problem domains. The book accomplishes all of this through a survey of broad subfields of ML, how each has attempted to be *the* master algorithm, has fallen short in some ways, but remains the best at some things and could play a role in the state of the art master algorithm (while acknowledging we're not quite there yet). So while the term 'master algorithm' is somewhat of a gimmick (as he acknowledges), it's a good way to think about what ML is attempting to accomplish as a field: building working, adaptive software systems with less and less human assistance by learning from data, and to see how many specific techniques have played a role in progress.What I don't know is how accessible this book might be to someone who's less technical. I think the first couple chapters would be a great read for anyone with a general interest, making clear how ML differs from the traditional software / automation that has brought us so far, but it could be that the details within the rest of the chapters that go into more depth would be too in the weeds.I've also read some other reviews from technical readers that assert the book lacks enough depth to be helpful, but this wasn't the case for me, in fact the level of detail was perfect—just deep enough to match with details I'd skimmed before in previous surveys of the field yet not so deep that I couldn't get through and enjoy the chapters in a casual evening read. The author also explained some concepts better than I've read anywhere else before, such as the debate is between frequentist and bayesian statisticians.

The Master Algorithm is a reasonable overview of the growing field of machine learning. The author explores the idea of there being a master algorithm which could explain everything given enough data. The idea seems a bit over the top when initially phrased but the real purpose of the book is to introduce ideas used in machine learning and how they work and are used to solve problems.The book is split into multiple chapters which start from discussing abstractly the master algorithm and then move on to some of the philosophical issues associated with using such algorithms. In particular the author discusses at the core of believing in pattern recognition algorithms is belief in inductive reasoning. The author discusses human learning and gets into some neuroscience and how neural networks are constructed. The reader gets a vague sense of Hebbian learning and how neuron weighting are at the core of neural networks. The author spends a lot of time discussing various approaches in machine learning and gives the reader an intuitive feel of Bayesian learning. The author was an originator in a particular algorithm called naïve Bayes which greatly simplified solutions to certain problems and so the author introduces his ideas to the reader. Other machine learning ideas are introduced like genetic programming and multivariable regression. The author also discusses other machine learning algorithms which turn data into a vector and then look for close neighbors of the vector to classify the input. The author also spends some time on how unsupervised learning would look. The book combines computer science ideas and intuition and tries to use a fictitious robot as the means to convey ideas about how a computer would learn. The author finally introduces his own master algorithm called alchemy which combines most of the models described in the book. The reader really gets little actual sense of what's going on in the algorithm as the author qualifies one needs a PhD in computer science.The Master Algorithm is the first book I have seen which introduces some of the ideas being used in machine learning to a general audience. It does so quite well and most of the ideas are absorbable. At the same time there are a few too many instances where the author is self promoting talking about all of the brilliant ideas he has had which have reshaped the field and how other areas of AI research of the past or Kurzweil and his singularity concept are idiotic. Despite probably being right in much of his analysis its arguing with no one on the other side and unproductive. Also the flavor of the writing is odd - it turns into some fantasy literature at times as though that makes the subject more digestible and in fact makes it more irritating. I enjoyed reading aspects of the book and do think the parts on what different schools of machine learning focus on are well written for a non expert, unfortunately there are many other parts of the book which one wants to get through as quickly as possible.

AWESOME OVERVIEW OF ML IDEASI have read a few textbooks on machine learning (intro to Statistical Learning by Hastie etc) and so I would say that my knowledge of ML is at the "textbook overview" level. Since I am not a ML practitioner, I may not be the best judge of a book such as this one, it was a fairly difficult read, and I know I need to read the book a second time to get an even greater appreciation and understanding of the concepts covered by the author. That being said, it was a very enjoyable book. The book was very different from any ML book that I've read or checked out either at a bookstore or online. I think one needs to have some knowledge of ML to appreciate the book - concepts like supervised learning, unsupervised learning, Bayesian inference, support vector machines, neural networks, etc .... The book deserves a 5-star rating because it added a lot of value to my understanding of ML, and increased my desire and curiosity to learn more about the field of ML ....

This is a great book and covers everything but this book is certainly not for someone without prior knowledge of machine learning and good understand of some principles.At the start of the book, Pedro mentions that this book is written for the layman but it's not.I gave this book to a friend of mine before I read it and he was like "doode this is not for laymen, I didn't understand a thing".While the book is great for seasoned programmers and those starting with machine learning and good knowledge of some concepts, this book ain't for someone who wants to know more about machine learning and algorithms.I still give it 5 because Pedro is a great guy and this book is indepth.

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