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Machine Learning Projects for .NET Developers Vital Source e-bog
Mathias Brandewinder
(2015)
Machine Learning Projects for .NET Developers Vital Source e-bog
Mathias Brandewinder
(2015)
Machine Learning Projects for .NET Developers Vital Source e-bog
Mathias Brandewinder
(2015)
Machine Learning Projects for . NET Developers
Mathias Brandewinder
(2015)
Sprog: Engelsk
Detaljer om varen
- Vital Source searchable e-book (Fixed pages)
- Udgiver: Springer Nature (Juli 2015)
- ISBN: 9781430267669
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
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Detaljer om varen
- Vital Source 180 day rentals (fixed pages)
- Udgiver: Springer Nature (Juli 2015)
- ISBN: 9781430267669R180
Bookshelf online: 180 dage fra købsdato.
Bookshelf appen: 180 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
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Detaljer om varen
- Vital Source 365 day rentals (fixed pages)
- Udgiver: Springer Nature (Juli 2015)
- ISBN: 9781430267669R365
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: 5 år fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Paperback: 300 sider
- Udgiver: Apress L. P. (Juni 2015)
- ISBN: 9781430267676
Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You'll code each project in the familiar setting of Visual Studio, while the machine learning logic uses F#, a language ideally suited to machine learning applications in .NET. If you're new to F#, this book will give you everything you need to get started. If you're already familiar with F#, this is your chance to put the language into action in an exciting new context.
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#'s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language)
- Transform your data into informative features, and use them to make accurate predictions
- Find patterns in data when you don't know what you're looking for
- Predict numerical values using regression models
- Implement an intelligent game that learns how to play from experience
Along the way, you'll learn fundamental ideas that can be applied in all kinds of real-world contexts and industries, from advertising to finance, medicine, and scientific research. While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches. If you enjoy hacking code and data, this book is for you.
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