Machine learning projects for .NET Developers. (2015)

• Record Type: Book
• Further Information: Note: Mathias Brandewinder.
• Authors: Brandewinder, Mathias
• Contents: 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.
• Publisher Details: Berkeley, CA : Apress
• Publication Date: 2015
• Copyright Date: 2015
• Extent: 1 online resource (xix, 275 pages), illustrations
• Subjects: 006.3/1; Computer science; Machine learning; COMPUTERS -- General; Logiciels; Informatique; Langages de programmation; Machine learning; Computers -- Software Development & Engineering -- General; Computers -- Intelligence (AI) & Semantics; Software Engineering; Artificial intelligence; Artificial intelligence; Software engineering; Electronic books
• Languages: English
• ISBNs: 9781430267669; 1430267666; 1430267674; 9781430267676
• Related ISBNs: 9781430267676
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed July 13, 2015).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.