Get Free Ebook Paper Towns, by John Green

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Paper Towns, by John Green

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Review

Winner of the Edgar Award for Best Young Adult MysteryNew York Times bestsellerUSA Today bestsellerPublishers Weekly bestsellerA Booklist Best Book of the YearAn SLJ Best Book of the YearA VOYA Best Book of the Year“Green’s prose is astounding — from hilarious, hyperintellectual trash talk and shtick, to complex philosophizing, to devastating observation and truths.” —SLJ, starred review“[Green’s] a superb stylist, with a voice perfectly matched to his amusing, illuminating material.” —Booklist, starred review“Laugh-out-loud humor and heartfelt poignancy.”—Kliatt, starred review“Green delivers once again with this satisfying, crowd-pleasing look at a complex, smart boy and the way he loves. Genuine—and genuinely funny—dialogue, a satisfyingly tangled but not unbelievable mystery and delightful secondary characters.”—Kirkus"Stellar, with deliciously intelligent dialogue and plenty of mind-twisting insights…a powerfully great read." —VOYA "Compelling." —The Bulletin of the Center for Children's Books

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About the Author

John Green is the award-winning, #1 bestselling author of Looking for Alaska, An Abundance of Katherines, Paper Towns, Will Grayson, Will Grayson (with David Levithan), and The Fault in Our Stars. His many accolades include the Printz Medal, a Printz Honor, and the Edgar Award. He has twice been a finalist for the LA Times Book Prize. John was selected by TIME magazine as one of the 100 Most Influential People in the World. With his brother, Hank, John is one half of the Vlogbrothers (youtube.com/vlogbrothers), one of the most popular online video projects in the world. You can join the millions who follow John on Twitter (@johngreen) and tumblr (fishingboatproceeds.tumblr.com) or visit him online at johngreenbooks.com. John lives with his family in Indianapolis, Indiana.

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Product details

Paperback: 305 pages

Publisher: Speak; Reprint edition (September 22, 2009)

Language: English

ISBN-10: 9780142414934

ISBN-13: 978-0142414934

ASIN: 014241493X

Product Dimensions:

5.5 x 0.9 x 8.2 inches

Shipping Weight: 12 ounces (View shipping rates and policies)

Average Customer Review:

4.2 out of 5 stars

5,364 customer reviews

Amazon Best Sellers Rank:

#7,435 in Books (See Top 100 in Books)

I liked this book, but it was a little weird with the plot. It kept my attention, but I expected better after reading Fault in our Stars and Looking for Alaska, both of which I loved. I do love Green's writing and character development, and this book continued to impress with that.

Paper Towns by John Green tells the story of Quentin, otherwise known as Q. Q and his next door neighbor Margo used to be best friends and, as they’ve grown up and become high school seniors, they have turned into acquaintances. One night, Margo talks Q into helping her seek revenge on her ex-boyfriend and ex-best friend, who happen to be sneaking around behind her back to have sex together. After their fun and rowdy late night, Q is anxious to see if Margo acts differently towards him at school. When she’s not at school or even at home for a couple of days, everyone assumes Margo is on just another one of her adventures. As Q tries to figure out and follow the clues, he begins to worry that he might find Margo dead. The ongoing suspense along with the wonderfully executed humor have made this one of my favorite books from beginning to end. Dynamic, complex, real characters bring depth, realism and humor into this adventure of a story-5 Stars!

I give this book a 2 1/12 star (rather than a 2) because I liked the writing style, which I found to be entertaining. The story arc? Not so much. I actually thought it was pretty good up to the point where Margo disappears, which is fairly early in the story. But from then on, the story spins it's wheels, while the characters obsess about prom, get drunk, over-think things, and follow clues that go nowhere, until the story finally limps to an unimpressive climax.SPOILER ALERT:Margo is ultimately found. But why she preferred to run away days before completing her HS diploma to live in scary, decaying, boarded-up buildings and write in notebooks didn't really resonate with me. Other than her parents being intolerant asses and not understanding her, in the absence of any terrible psychological, emotional, or physical trauma, I couldn't fully appreciate (as presented) her appeal for this dreary and lonely lifestyle. Although she disappears early in the book, she's a big presence in the book, talked about and obsessed about constantly by Q and the other characters. She's revered as a "legend" and arguably one of the more interesting characters in the book - if not by presence, by reputation. So there's all this mystery and build up around her. The allure of her bigger-than- life persona. Her fearless wild actions. It all sets up a series of questions that begs for dramatic answers. Where did she go and why? Did she run away? Was she dead? Was she off doing wondrous bold things?After a drawn out circle-jerk of clues, that made little sense at all- they finally find her and...pffffft. Her reasons for running aren't particularly interesting or strong - which was a major let down. Equally unsatisfactory: What ultimately happens to Margo from there? After a kind of climactic "wrap it up" talk between Margo and Q, which I guess, is supposed to be deep, about how much Q has grown brave because of her and how no one really knew or saw Margo for who she is, insert metaphors, literary symbolism, and more metaphors, they make pledges to email and stay in touch, etc etc etc - Q and his merry crew leave Margo where they found her - in a small NY town living in a deserted decaying building with no electricity or plumping (she has to go to the nearby truck stop to shower) which is, I guess where she wants to be. [ Apparently, planning to eventually move to NYC or something] She seems pretty depressed to me. What's to become of her? We never know. But I guess Margo's not supposed to be the point, although you kinda wish she was, after all, she's pretty much the main focus of Q's inner dialog ad nauseam throughout the entire book. But it's Q's journey and how his idealization of Margo drives him. I just wish his journey had been more interesting.BTW, the whole Paper Town analogy didn't quite resonate for me either. Something about suburban new development , or....something. To be fair, I was skimming rapidly by this time, having tired of the clues to no where, so maybe I missed it.

Of the John Green books I have read, this is my favorite. We have a familiar cast of characters -- the nerdy teenage boy and his brainiac friends and the damaged teenage girl who is may be popular and confident on the outside but is deeply troubled on the inside. We also have a lot of smart dialogue, a mystery, a quest and the anguish and sweetness of young love. But in this book, it somehow comes together, aided by the musings of Walt Whitman, is a way that is not treacly or weepy - but real and grounded.The book is dominated by Margo, a high school queen bee whose brash exterior hides an intellectual and angst-filled interior. The male lead is Quentin, brainy but balanced. The two live next door but are in different social sects in the high school caste system. However Quentin carries a torch for his childhood friend. After an extraordinary night of adventure together a month before graduation, Margo disappears leaving some cryptic clues as to her whereabouts. It is for Quentin to follow the trail -- but to find Margo he first has to understand Margo, not as an ideal or love object or symbol -- but the real person.The climactic scenes of this quest are extraordinarily well done and the final resolution is moving without being shattering. Really enjoyed this one.

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PDF Ebook Advanced Analytics with Spark: Patterns for Learning from Data at Scale

PDF Ebook Advanced Analytics with Spark: Patterns for Learning from Data at Scale

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Advanced Analytics with Spark: Patterns for Learning from Data at Scale

PDF Ebook Advanced Analytics with Spark: Patterns for Learning from Data at Scale

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About the Author

Sandy Ryza is a data scientist at Cloudera and active contributor to the Apache Spark project. He recently led Spark development at Cloudera and now spends his time helping customers with a variety of analytic use cases on Spark. He is also a member of the Hadoop Project Management Committee.Uri Laserson is a data scientist at Cloudera, where he focuses on Python in the Hadoop ecosystem. He also helps customers deploy Hadoop on a wide range of problems, focusing on life sciences and health care. Previously, Uri cofounded Good Start Genetics, a next generationdiagnostics company while working towards a PhD in biomedical engineering at MIT.Sean Owen is Director of Data Science for EMEA at Cloudera. He has been a significant contributor to the Apache Mahout machine learning project since 2009, and authored its “Taste” recommender framework. He created the Oryx (formerly Myrrix) project for realtime large scale learning on Hadoop, built on lambda architecture principles, and has contributed to Spark and Spark’s MLlib project.Josh Wills is Cloudera's Senior Director of Data Science, working with customers and engineers to develop Hadoop based solutions across a wide range of industries. He is the founder and VP of the Apache Crunch project for creating optimized MapReduce and Spark pipelines in Java.Prior to joining Cloudera, Josh worked at Google, where he worked on the ad auction system and then led the development of the analytics infrastructure used in Google+.

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Product details

Paperback: 276 pages

Publisher: O'Reilly Media; 1 edition (April 20, 2015)

Language: English

ISBN-10: 1491912766

ISBN-13: 978-1491912768

Product Dimensions:

7 x 0.6 x 9.2 inches

Shipping Weight: 1 pounds

Average Customer Review:

4.3 out of 5 stars

33 customer reviews

Amazon Best Sellers Rank:

#500,214 in Books (See Top 100 in Books)

This book fills an important gap in large scale data science.Spark has emerged as the big data platform of choice for data scientists both from the ease of use as well as the performance / optimization point of view. In a few lines of Scala code, Spark allows you to write iterative algorithms that scale out very well. For a data scientist who wants to explore large scale data sets, Spark is a great starting point (this is incredible progress in the Spark community given the project is just about 4 years old). However, Spark itself is moving fast and maturing with time, and Spark and Scala as well as distributed algorithms are typically not in the arsenal of many data scientists today.What this book does is teach you how to think about data science problems at scale, in the context of Spark. By well chosen examples covering both supervised and unsupervised learning, the authors take you step by step from a practical problem definition (say how to recommend music given user's history of music listened to) to what features are relevant, what machine learning algorithm to use and how to tune parameters to optimize the solution and how you can use Spark to do all of this in an interactive / iterative manner. As a bonus, they also point you to well engineered data sets that you can use to follow along the discussion and learn by trying out the examples yourself.By embracing the feature engineering steps and data cleaning/ error handling and tuning /feedback steps, the authors manage to show how real world data science works and how you can do full stack data science using Spark and gain immensely from the interactive nature of the Spark REPL.Overall, I highly recommend this book, and though it is the first book on Data Science using Spark, it sets a high standard for subsequent efforts.

TL;DR If you are looking for a intro to data science, data analysis and machine learning at scale - this is the right book. Sure, there are others, maybe more popular books from O'Reilly considering these topics, but the authors of those are using R and Python and the books are not focused on the performance and scalability. For closer details regarding Spark you can also take a look at this introductory Spark book - Learning Spark.This book presents 9 case studies of data analysis applications in various domains. The topics are diverse and the authors always use real world datasets. Beside learning Spark and a data science you will also have the opportunity to gain insight about topics like taxi traffic in NYC, deforestation or neuroscience. Without any previous exposure or contact with machine learning readers might struggle to understand certain chapters, so I think it's good idea to actually try those examples yourself while reading and Google for further details about the used methods. Many of the chapters end only with basic models, which barely outperform the baselines, so if you want to, there is a lot of space for their improvement and further work.Spark itself provides it's users with APIs in three languages - Java, Scala and Python. This books successfully covers each one of these, although you can feel slight preference of a Scala throughout the book. For Scala starters - they always explain some of the special constructs or syntax features which is in fact a nice thing. Introduction and Appendix chapters provides basic information about the Spark core, RDDs (Resilient distributed datasets) or options of running Spark - whether in cluster (Mesos, YARN, Spark's own) or standalone settings. Throughout the book you can find some really worthy tips about Spark or data analysis - like using other serializer than the Java's default (they recommend kryo), overview of data cleansing and whole machine learning pipeline. To sum up, I recommend this book to every data scientist - because it demonstrates advanced topics like workload distribution and scaling on an enjoyable examples.

It is a so, so book. Examples are okay and the codes provided are "elegant" - certainly the result of spending hours and hours optimizing them; but that is not what a typical Spark users will face in life. The explanations are hurried and they make it very hard for the reader to connect the dots. It seems that the book's intent was right, but the application was woefully inadequate. If you do all the work in the book, you will be very competent at reading csv files - but is about all. The authors have a habit of providing esoteric "helper" functions to clean up the files but you don't really understand what is happening because either the explanations are thin or there is none to be found. A big part of data science is preparing the data - anyone can turn the crank on clean data but how do you go from the start to finish. This was their opportunity and they left a big gap. Spark's ML examples are nicer than what is presented in this book; paying for a book to get minimal information is a bit odd. I was really looking forward to going through this book and I am glad I did; it makes me appreciate authors who spend time writing good books.

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