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|a 1784396362
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|a 1784399086
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|a 9781784399085
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|a Bozonier, Justin
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|a Test-driven machine learning
|b control your machine learning algorithms using test-driven development to achieve quantifiable milestones
|c Justin Bozonier
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|a Control your machine learning algorithms using test-driven development to achieve quantifiable milestones
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|a Birmingham, UK
|b Packt Publishing
|c 2015
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|a 1 online resource
|b illustrations
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|a Cover ; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introducing Test-Driven Machine Learning; Test-driven development; The TDD cycle; Red; Green; Refactor; Behavior-driven development; Our first test; The anatomy of a test; Given; When; Then; TDD applied to machine learning; Dealing with randomness; Different approaches to validating the improved models; Classification overview; Regression; Clustering; Quantifying the classification models; Summary; Chapter 2: Perceptively Testing a Perceptron; Getting started; Summary
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|a Creating a classifier chooser (it needs to run tests to evaluate classifier performance)Getting choosey; Developing testable documentation; Decision trees; Summary; Chapter 9: Bringing It All Together; Starting at the highest level; The real world; What we've accomplished; Summary; Index
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|a Chapter 3: Exploring the Unknown with Multi-armed BanditsUnderstanding a bandit; Testing with simulation; Starting from scratch; Simulating real world situations; A randomized probability matching algorithm; A bootstrapping bandit; The problem with straight bootstrapping; Multi-armed armed bandit throw down; Summary; Chapter 4: Predicting Values with Regression; Refresher on advanced regression; Regression assumptions; Quantifying model quality; Generating our own data; Building the foundations of our model; Cross-validating our model; Generating data; Summary
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|a Chapter 5: Making Decisions Black and White with Logistic RegressionGenerating logistic data; Measuring model accuracy; Generating a more complex example; Test driving our model; Summary; Chapter 6: You're So Naïve, Bayes; Gaussian classification by hand; Beginning the development; Summary; Chapter 7: Optimizing by Choosing a New Algorithm; Upgrading the classifier; Applying our classifier; Upgrading to Random Forest; Summary; Chapter 8: Exploring Scikit-learn Test First; Test-driven design; Planning our journey
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|a Computer algorithms / fast
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|a Algorithms
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|a COMPUTERS. / Databases / Data Mining / bisacsh
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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|a Computer algorithms / http://id.loc.gov/authorities/subjects/sh91000149
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|a COMPUTERS. / Machine Theory / bisacsh
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|a Algorithmes
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|a algorithms / aat
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|a Machine learning / fast
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|a Apprentissage automatique
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|a COMPUTERS. / Intelligence (AI) & Semantics / bisacsh
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|a Machine Learning
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|a eng
|2 ISO 639-2
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|b OREILLY
|a O'Reilly
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|a Community experience distilled
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|a Includes index
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|a GBC1J1186
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|z 9781784396367
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|u https://learning.oreilly.com/library/view/~/9781784399085/?ar
|x Verlag
|3 Volltext
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|a 006.31
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|a 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 Take 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.
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|a Those looking for examples of how to isolate issues in models and improve them will find ideas in this book to move forward.
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|a Machine learning is applicable to a lot of what you do every day. As a result, you can't take forever to deliver your first iteration of software. Learning to build machine learning algorithms within a controlled test framework will speed up your time to deliver, quantify quality expectations with your clients, and enable rapid iteration and collaboration. This book will show you how to quantifiably test machine learning algorithms. The very different, foundational approach of this book sta..
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|a Control your machine learning algorithms using test-driven development to achieve quantifiable milestones About This Book 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 Naïve 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. You may be starting, or have already started, a machine learning project at work and are looking for a way to deliver results quickly to enable rapid iteration and improvement.
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