Bisher 41,99**
36,99
versandkostenfrei*

Alle Preise in Euro, inkl. MwSt.
**Früherer Preis
Sofort lieferbar
18 °P sammeln

    Broschiertes Buch

Software that learns from experience can improve far beyond what a single developer, or even a large team, can write into its code. This is machine learning and it's already influencing a huge range of industries, from advertising to finance, medicine and the most cutting edge scientific research. Machine Learning Projects for .NET Developers is your practical and accessible introduction to this exciting area of software development. The book emphasizes a functional style of coding that promotes bug-free, reusable code that can be easily parallelized for scalable performance. You'll code each…mehr

Produktbeschreibung
Software that learns from experience can improve far beyond what a single developer, or even a large team, can write into its code. This is machine learning and it's already influencing a huge range of industries, from advertising to finance, medicine and the most cutting edge scientific research. Machine Learning Projects for .NET Developers is your practical and accessible introduction to this exciting area of software development. The book emphasizes a functional style of coding that promotes bug-free, reusable code that can be easily parallelized for scalable performance. You'll code each project in the familiar setting of a C sharp application, while the machine learning logic uses F sharp, a language ideally suited to machine learning applications and the logical choice for machine learning in .NET. If you're new to F sharp, this book will give you everything you need to get started. If you're already familiar with F sharp, this is your chance to put the language into action in an exciting new context, discover new techniques, and find out how seamlessly it can integrate with your C sharp applications. In a series of fascinating projects, you'll learn how to: Build an optical character recognition (OCR) system from scratch Code a spam filter that learns by example Use F sharp's powerful type providers to interface seamlessly with external resources (in this case, useful data analysis tools from the R programming language) Clean up incomplete data and use it to make accurate predictions Build a smart recommendation engine Find patterns in data when you don't know what you're looking for Predict numerical values using regression models Accurately spot trends and anomalies Along the way, you'll have fun hacking at data, learn fundamental ideas that can be applied in a broad range of real-world contexts, and discover new ways to simplify and approach real-world coding challenges. With Machine Learning Projects for .NET Developers , you'll expand your skill set as a .NET developer, gain a new understanding of data, and have fun working on challenging, mind-expanding problems!
  • Produktdetails
  • Verlag: Springer, Berlin; Apress
  • 1st ed.
  • Seitenzahl: 300
  • Erscheinungstermin: 29. Juni 2015
  • Englisch
  • Abmessung: 254mm x 179mm x 22mm
  • Gewicht: 576g
  • ISBN-13: 9781430267676
  • ISBN-10: 1430267674
  • Artikelnr.: 40356956
Autorenporträt
Mathias Brandewinder is a Microsoft MVP for F# based in San Francisco, California. An unashamed math geek, he became interested early on in building models to help others make better decisions using data. He collected graduate degrees in Business, Economics and Operations Research, and fell in love with programming shortly after arriving in the Silicon Valley. He has been developing software professionally since the early days of .NET, developing business applications for a variety of industries, with a focus on predictive models and risk analysis.
Inhaltsangabe
Chapter 1: 256 Shades of Gray : Building A Program to Automatically Recognize Images of Numbers

Chapter 2: Spam or Ham? Detecting Spam in Text Using Bayes' Theorem

Chapter 3: The Joy of Type Providers: Finding and Preparing Data, From Anywhere

Chapter 4: Of Bikes and Men: Fitting a Regression Model to Data with Gradient Descent

Chapter 5: You Are Not An Unique Snowflake: Detecting Patterns with Clustering and Principle Component Analysis

Chapter 6: Trees and Forests: Making Predictions from Incomplete Data

Chapter 7: A Strange Game: Learning From Experience with Reinforcement Learning

Chapter 8: Digits, Revisited: Optimizing and Scaling Your Algorithm Code

Chapter 9: Conclusion