LEADER 06843nam 22007335 450 001 9910300642003321 005 20200701061144.0 010 $a9781430267669 010 $a1430267666 024 7 $a10.1007/978-1-4302-6766-9 035 $a(CKB)3710000000443975 035 $a(EBL)3567650 035 $a(SSID)ssj0001534492 035 $a(PQKBManifestationID)11873093 035 $a(PQKBTitleCode)TC0001534492 035 $a(PQKBWorkID)11494620 035 $a(PQKB)11436593 035 $a(DE-He213)978-1-4302-6766-9 035 $a(MiAaPQ)EBC3567650 035 $a(CaSebORM)9781430267669 035 $a(PPN)187683603 035 $a(OCoLC)919515488 035 $a(OCoLC)ocn919515488 035 $a(EXLCZ)993710000000443975 100 $a20150709d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning Projects for .NET Developers /$fby Mathias Brandewinder 205 $a1st ed. 2015. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2015. 215 $a1 online resource (290 p.) 225 1 $aExpert's Voice in .NET 300 $aIncludes index. 311 08$a9781430267676 311 08$a1430267674 327 $aContents 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 327 $aIntroducing 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? 327 $aOur 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; Nai?vely 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 327 $aImplementing 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 327 $aAnalyzing 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 327 $aStochastic Gradient Descent 330 $aMachine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You?ll code each project in the familiar setting of Visual Studio, while the machine learning logic uses F#, a language ideally suited to machine learning applications in .NET. If you?re new to F#, this book will give you everything you need to get started. If you?re already familiar with F#, this is your chance to put the language into action in an exciting new context. In a series of fascinating projects, you?ll learn how to: Build an optical character recognition (OCR) system from scratch Code a spam filter that learns by example Use F#?s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language) Transform your data into informative features, and use them to make accurate predictions Find patterns in data when you don?t know what you?re looking for Predict numerical values using regression models Implement an intelligent game that learns how to play from experience Along the way, you?ll learn fundamental ideas that can be applied in all kinds of real-world contexts and industries, from advertising to finance, medicine, and scientific research. While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches. If you enjoy hacking code and data, this book is for you. 410 0$aExpert's voice in .NET. 606 $aArtificial intelligence 606 $aSoftware engineering 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aSoftware Engineering/Programming and Operating Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I14002 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aArtificial intelligence. 615 0$aSoftware engineering. 615 14$aArtificial Intelligence. 615 24$aSoftware Engineering/Programming and Operating Systems. 615 24$aArtificial Intelligence. 676 $a004 700 $aBrandewinder$b Mathias$4aut$4http://id.loc.gov/vocabulary/relators/aut$0959571 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910300642003321 996 $aMachine Learning Projects for .NET Developers$92174433 997 $aUNINA