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Machine learning projects for .NET developers /
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 ma...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[New York, NY] :
Apress,
[2015]
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| Colección: | Expert's voice in .NET.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: 256 Shades of Gray; What Is Machine Learning?; A Classic Machine Learning Problem: Classifying Images; Our Challenge: Build a Digit Recognizer; Distance Functions in Machine Learning; Start with Something Simple; Our First Model, C# Version; Dataset Organization; Reading the Data; Computing Distance between Images; Writing a Classifier; So, How Do We Know It Works?; Cross-validation; Evaluating the Quality of Our Model; Improving Your Model
- Introducing F# for Machine Learning Live Scripting and Data Exploration with F# Interactive; Creating our First F# Script; Dissecting Our First F# Script; Creating Pipelines of Functions; Manipulating Data with Tuples and Pattern Matching; Training and Evaluating a Classifier Function; Improving Our Model; Experimenting with Another Definition of Distance; Factoring Out the Distance Function; So, What Have We Learned?; What to Look for in a Good Distance Function; Models Don't Have to Be Complicated; Why F#?; Going Further; Chapter 2: Spam or Ham?
- Our Challenge: Build a Spam-Detection Engine Getting to Know Our Dataset; Using Discriminated Unions to Model Labels; Reading Our Dataset; Deciding on a Single Word; Using Words as Clues; Putting a Number on How Certain We Are; Bayes' Theorem; Dealing with Rare Words; Combining Multiple Words; Breaking Text into Tokens; Naïvely Combining Scores; Simplified Document Score; Implementing the Classifier; Extracting Code into Modules; Scoring and Classifying a Document; Introducing Sets and Sequences; Learning from a Corpus of Documents; Training Our First Classifier
- Implementing Our First Tokenizer Validating Our Design Interactively; Establishing a Baseline with Cross-validation; Improving Our Classifier; Using Every Single Word; Does Capitalization Matter?; Less Is more; Choosing Our Words Carefully; Creating New Features; Dealing with Numeric Values; Understanding Errors; So What Have We Learned?; Chapter 3: The Joy of Type Providers; Exploring StackOverflow data; The StackExchange API; Using the JSON Type Provider; Building a Minimal DSL to Query Questions; All the Data in the World; The World Bank Type Provider; The R Type Provider
- Analyzing Data Together with R Data Frames Deedle, a .NET Data Frame; Data of the World, Unite!; So, What Have We Learned?; Going Further; Chapter 4: Of Bikes and Men; Getting to Know the Data; What's in the Dataset?; Inspecting the Data with FSharp.Charting; Spotting Trends with Moving Averages; Fitting a Model to the Data; Defining a Basic Straight-Line Model; Finding the Lowest-Cost Model; Finding the Minimum of a Function with Gradient Descent; Using Gradient Descent to Fit a Curve; A More General Model Formulation; Implementing Gradient Descent