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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.
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.
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.
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
| Erscheinungsjahr: | 2015 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 300 |
| Inhalt: |
xix
300 S. 84 s/w Illustr. 300 p. 84 illus. |
| ISBN-13: | 9781430267676 |
| ISBN-10: | 1430267674 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Brandewinder, Mathias |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 254 x 178 x 17 mm |
| Von/Mit: | Mathias Brandewinder |
| Erscheinungsdatum: | 29.06.2015 |
| Gewicht: | 0,569 kg |
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.
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
| Erscheinungsjahr: | 2015 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 300 |
| Inhalt: |
xix
300 S. 84 s/w Illustr. 300 p. 84 illus. |
| ISBN-13: | 9781430267676 |
| ISBN-10: | 1430267674 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Brandewinder, Mathias |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 254 x 178 x 17 mm |
| Von/Mit: | Mathias Brandewinder |
| Erscheinungsdatum: | 29.06.2015 |
| Gewicht: | 0,569 kg |
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