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Machine Learning Projects for .NET Developers
Author: Mathias Brandewinder
File Type: pdf
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. Youll 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 youre new to F#, this book will give you everything you need to get started. If youre 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, youll learn how toullBuild an optical character recognition (OCR) system from scratchllCode a spam filter that learns by examplellUse F#s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language)llTransform your data into informative features, and use them to make accurate predictionsllFind patterns in data when you dont know what youre looking forllPredict numerical values using regression modelsllImplement an intelligent game that learns how to play from experiencelulAlong the way, youll 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.What youll learnullLearn vocabulary and landscape of machine learningllRecognize patterns in problems and how to solve themllLearn simple prediction algorithms and how to apply themllDevelop, diagnose and tune your modelsllWrite elegant, efficient and bug-free functional code with F#lulWho this book is forMachine Learning Projects for .NET Developers is for intermediate to advanced .NET developers who are comfortable with C#. No prior experience of machine learning techniques is required. If youre new to F#, youll find everything you need to get started. If youre already familiar with F#, youll find a wealth of new techniques here to interest and inspire you.While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches and how they can be used in actual code. If you enjoy hacking code and data, this book is for you. Table of ContentsChapter 1 256 Shades of Gray Building A Program to Automatically Recognize Images of NumbersChapter 2 Spam or Ham? Detecting Spam in Text Using Bayes TheoremChapter 3 The Joy of Type Providers Finding and Preparing Data, From AnywhereChapter 4 Of Bikes and Men Fitting a Regression Model to Data with Gradient DescentChapter 5 You Are Not An Unique Snowflake Detecting Patterns with Clustering and Principle Component AnalysisChapter 6 Trees and Forests Making Predictions from Incomplete Data Chapter 7 A Strange Game Learning From Experience with Reinforcement LearningChapter 8 Digits, Revisited Optimizing and Scaling Your Algorithm CodeChapter 9 Conclusion**
File Type: pdf
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. Youll 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 youre new to F#, this book will give you everything you need to get started. If youre 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, youll learn how toullBuild an optical character recognition (OCR) system from scratchllCode a spam filter that learns by examplellUse F#s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language)llTransform your data into informative features, and use them to make accurate predictionsllFind patterns in data when you dont know what youre looking forllPredict numerical values using regression modelsllImplement an intelligent game that learns how to play from experiencelulAlong the way, youll 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.What youll learnullLearn vocabulary and landscape of machine learningllRecognize patterns in problems and how to solve themllLearn simple prediction algorithms and how to apply themllDevelop, diagnose and tune your modelsllWrite elegant, efficient and bug-free functional code with F#lulWho this book is forMachine Learning Projects for .NET Developers is for intermediate to advanced .NET developers who are comfortable with C#. No prior experience of machine learning techniques is required. If youre new to F#, youll find everything you need to get started. If youre already familiar with F#, youll find a wealth of new techniques here to interest and inspire you.While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches and how they can be used in actual code. If you enjoy hacking code and data, this book is for you. Table of ContentsChapter 1 256 Shades of Gray Building A Program to Automatically Recognize Images of NumbersChapter 2 Spam or Ham? Detecting Spam in Text Using Bayes TheoremChapter 3 The Joy of Type Providers Finding and Preparing Data, From AnywhereChapter 4 Of Bikes and Men Fitting a Regression Model to Data with Gradient DescentChapter 5 You Are Not An Unique Snowflake Detecting Patterns with Clustering and Principle Component AnalysisChapter 6 Trees and Forests Making Predictions from Incomplete Data Chapter 7 A Strange Game Learning From Experience with Reinforcement LearningChapter 8 Digits, Revisited Optimizing and Scaling Your Algorithm CodeChapter 9 Conclusion**
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