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The Part 1 MRCOG covers basic science knowledge as it applies to obstetrics and gynaecology. There are two papers, both of which contain a mixture of single best answer questions (SBAs) and multiple choice questions (MCQs). This book provides a verbatim transcript for the MCQ element of each of the 10 papers set between 1997 and 2001, giving candidates the opportunity to test their knowledge and preparedness for the examination against real questions.

• Sales Rank: #523585 in Books
• Published on: 2004-12-01
• Released on: 2004-12-01
• Original language: English
• Number of items: 1
• Dimensions: 9.21" h x .47" w x 6.14" l, .80 pounds
• Binding: Paperback
• 218 pages

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• Build smart extensions to pre-existing features at work that can help maximize their value
• Quantify your models to drive real improvement
• Take your knowledge of basic concepts, such as linear regression and Naive Bayes classification, to the next level and productionalize their models
• Play what-if games with your models and techniques by following the test-driven exploration process

Who This Book Is For

This book is intended for data technologists (scientists, analysts, or developers) with previous machine learning experience who are also comfortable reading code in Python. This book is ideal for those looking for a way to deliver results quickly to enable rapid iteration and improvement.

What You Will Learn

• Get started with an introduction to test-driven development and familiarize yourself with how to apply these concepts to machine learning
• Build and test a neural network deterministically, and learn to look for niche cases that cause odd model behaviour
• Learn to use the multi-armed bandit algorithm to make optimal choices in the face of an enormous amount of uncertainty
• Generate complex and simple random data to create a wide variety of test cases that can be codified into tests
• Develop models iteratively, even when using a third-party library
• Quantify model quality to enable collaboration and rapid iteration
• Adopt simpler approaches to common machine learning algorithms
• Use behaviour-driven development principles to articulate test intent

In Detail

Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences.

The book begins with an introduction to test-driven machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression. You will discover how to test different approaches to Naive Bayes and compare them quantitatively, along with learning how to apply OOP (Object Oriented Programming) and OOP patterns to test-driven code, leveraging scikit-Learn.

Finally, you will walk through the development of an algorithm which maximizes the expected value of profit for a marketing campaign, by combining one of the classifiers covered with the multiple regression example in the book.

• Sales Rank: #1431999 in eBooks
• Published on: 2015-11-27
• Released on: 2015-11-27
• Format: Kindle eBook

About the Author

Justin Bozonier

Justin Bozonier is a data scientist living in Chicago. He is currently a Senior Data Scientist at GrubHub. He has led the development of their custom analytics platform and also led the development of their first real time split test analysis platform which utilized Bayesian Statistics. In addition he has developed machine learning models for data mining as well as for prototyping product enhancements. Justin's software development expertise has earned him acknowledgements in the books Parallel Programming with Microsoft� .NET as well as Flow-Based Programming, Second Edition. He has also taught a workshop at PyData titled Simplified Statistics through Simulation. His previous work experience includes being an Actuarial Systems Developer at Milliman, Inc., contracting as a Software Development Engineer II at Microsoft, and working as a Sr. Data Analyst and Lead Developer at Cheezburger Network amongst other experience.

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1 of 1 people found the following review helpful.
Completely different approach to ML
By Julian Cook
This is a totally out-of-left-field book on machine learning. Not only is it novel, but I think the ideas will become more important over time, as more models move into production scenarios and need to be iteratively changed and improved, whilst not _breaking_ the model that already works.
The main idea, as you may have guessed, is to change the model development process from exploring data - followed by trying different models, to a more formal approach of wrapping your data in a test framework and then proceeding to develop the model(s).
The initial model (or failing test) can just be a random guesser, after this you continually re-factor your code to improve on the random result or previous iteration of the model (at which point the test passes).
This will seem weird to a statistician, who would insist of emphasizing significance tests or (at least) looking at an ROC curve, which this book also does. The point made by the author is that once you enter the real world of running prediction models in production, you need to move to a process of iterative development, where you have some formal guarantee that the new model is better than the last and that you have not somehow fooled yourself into using something that is not much better than random.
There are a couple of drawbacks to the whole approach though. The first is the pain of setting up the tests in the first place, especially when you have a lot of data. I thought about setting up some synthetic data to test a new model, but it was honestly quicker to step through an example with the real data to figure out whether the code was working or not.
The other issue is with interactive environments, like R and ipython, you can write your code in short sections at the console, then test each section, or model iteration, as you go. This is very efficient and doesn't necessarily work with a 'think up the tests before you do anything' approach.
Even if you don't like the ideas, the book itself is still a very good way to approach statistical model development and should be widely read, especially in organizations where multiple people might touch the ML code.

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• Published on: 2008-05-06
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• Sales Rank: #627783 in Books
• Published on: 1991-09-03
• Original language: English
• Number of items: 1
• Dimensions: 9.02" h x .59" w x 5.98" l, .99 pounds
• Binding: Paperback
• 256 pages

About the Author
Andrew S. Horton is Professor of English at Loyola University, New Orleans. He is the author of George Roy Hill and the forthcoming Soviet Cinema under Glasnost.

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Once (NONE), by Enda Walsh, Glen Hansard, Markéta Irglová

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"A jewel box of a musical: small, delicate, brimming with emotion and charm."—Vogue

"It may sound like heresy to fans of the 2006 film, but this bewitching stage adaptation arguably improves on the movie, expanding its emotional breadth and elevating it stylistically while remaining true to the original's raw fragility."?Hollywood Reporter

Retaining the film's popular music and lyrics, acclaimed Irish playwright Enda Walsh adapts this charming tale of a complicated romance between an Irish street musician and a young Czech immigrant for the stage. A hit musical Off-Broadway, Once premiered on Broadway in spring 2012 to rave reviews.

Enda Walsh is the author of five Edinburgh Festival Fringe First Award-winning plays, including Penelope, The Walworth Farce, and The New Electric Ballroom. He also co-wrote the film Hunger, which won the Camera d'Or award at the Cannes Film Festival.

Glen Hansard and Mark�ta Irglov� are the stars and songwriters of the 2006 film Once, for which they won an Academy Award for Best Song. The two comprise the musical folk-rock duo The Swell Season, which is currently touring the United States. A documentary film of the duo, The Swell Season, was an official selection of the 2011 Tribeca Film Festival. Hansard is also a member of the Irish band The Frames and Irglov� is a classically trained Czech pianist and vocalist.

• Sales Rank: #670480 in eBooks
• Published on: 2013-01-11
• Released on: 2013-01-11
• Format: Kindle eBook

Review
?It may sound like heresy to fans of the 2007 film, but this bewitching stage adaption arguably improves on the movie, expanding its emotional breadth and elevating it stylistically while remaining true to the original?s raw fragility.? ? Hollywood Reporter (2013-01-17)

?A jewel box of a musical: small, delicate, brimming with emotion and charm.? ? Vogue (2013-01-17)

About the Author

Most helpful customer reviews

0 of 0 people found the following review helpful.
I wish I could review with a 6 star rating
By rathwulf
Because I would certainly would. Once, started as a movie a heart wrenching gorgeous movie about how people can bond through music. Its a universal theme as people form affections through bond and love of something mutual. But this work of art took it to another level.

Now the book is pretty much the movie but with certain plot details added, a joke or two to make it smoother but the beauty, the core, the heart and the ultimate expressions of love are there.

Indeed the book and the movie complement each other, and music changes sligthly in the musical so you dont feel it like a movie turned musical automatically upon seeing it.

**spoilers ahead**

**spoilers ahead**

**spoilers ahead**

The ending is pretty much the same, they indeed dont end up together, you know they will do so in the future. Because they love each other, but they still have things to work out with their respective former loved ones. But they are not right for each other. The power is there, the strength of the movie is translated to the play, because when the guy tells the girl about being alone I was moved to tears for I felt his loneliness and when the girl tells him her true feeling in the shadow of her language I cried "Once" more because I felt her regret. The play is strong and stands for itself a an amazing work of beautiful art.

1 of 1 people found the following review helpful.
Just...great.
By flapjackjoe
I'm NOT a reader. So for me to actually read this took a lot of internal motivation. I will admit, the book sucks. I mean, me reading the book to myself sucks. But the story is just incredible, and the show is even better. If you've not seen ONCE, you need to see it at least TWICE. It's one of the most intimate theatrical experiences you'll ever have. If a group of actors showed up to my house on any night to read this to me, and I'd be OK with that.

0 of 0 people found the following review helpful.
Best. Show. Ever.
By Camille Chartier
If I could give "Once" a million stars, I would. This is the best piece of theatre that I've ever experienced. Owning the script to such a beautiful masterpiece is so exciting. If you ever get the chance to see "Once" live, take it. It's the most beautiful and magical experience you'll ever have

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