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Hands-On Machine Learning with ML.NET
Machinelearning(ML)iswidelyusedinmanyindustriessuchasscience,healthcare,andresearchanditspopularityisonlygrowing.InMarch2018,MicrosoftintroducedML.NETtohelp.NETenthusiastsinworkingwithML.Withthisbook,you’llexplorehowtobuildML.NETapplicationswiththevariousMLmodelsavailableusingC#code.ThebookstartsbygivingyouanoverviewofMLandthetypesofMLalgorithmsused,alongwithcoveringwhatML.NETisandwhyyouneedittobuildMLapps.You’llthenexploretheML.NETframework,itscomponents,andAPIs.ThebookwillserveasapracticalguidetohelpingyoubuildsmartappsusingtheML.NETlibrary.You’llgraduallybecomewellversedinhowtoimplementMLalgorithmssuchasregression,classification,andclusteringwithreal-worldexamplesanddatasets.Eachchapterwillcoverthepracticalimplementation,showingyouhowtoimplementMLwithin.NETapplications.You’llalsolearntointegrateTensorFlowinML.NETapplications.Lateryou’lldiscoverhowtostoretheregressionmodelhousingpricepredictionresulttothedatabaseanddisplaythereal-timepredictedresultsfromthedatabaseonyourwebapplicationusingASP.NETCoreBlazorandSignalR.Bytheendofthisbook,you’llhavelearnedhowtoconfidentlyperformbasictoadvanced-levelmachinelearningtasksinML.NET.
最新章節(jié)
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- Other Books You May Enjoy
- Summary
- Utilizing the full YOLO model
- Image scaling
- Logging
品牌：中圖公司
上架時間：2021-06-24 15:28:52
出版社：Packt Publishing
本書數(shù)字版權(quán)由中圖公司提供，并由其授權(quán)上海閱文信息技術(shù)有限公司制作發(fā)行
- Leave a review - let other readers know what you think 更新時間：2021-06-24 16:44:15
- Other Books You May Enjoy
- Summary
- Utilizing the full YOLO model
- Image scaling
- Logging
- Exploring additional production application enhancements
- Running the application
- The MainWindowViewModel class
- The ImageClassificationPredictor class
- The MainWindow.xaml file
- The YoloBoundingBox class
- The DimensionsBase class
- Diving into the code
- Exploring the project architecture
- Creating the ONNX object detection application
- The YOLO ONNX model
- Introducing ONNX
- Breaking down ONNX and YOLO
- Using ONNX with ML.NET
- Summary
- Utilizing a database
- Logging
- Self-training based on the end user's input
- Additional ideas for improvements
- Running the image classification application
- The ImageClassificationPredictor class
- The ImageDataPredictionItem class
- The ImageDataInputItem class
- The BaseML class
- The MainWindow.xaml.cs file
- The MainWindow.xaml class
- The MainWindowViewModel class
- Diving into the WPF image classification application
- Exploring the project architecture
- Creating the WPF image classification application
- Breaking down Google's Inception model
- Using TensorFlow with ML.NET
- Summary
- Apache Spark
- Apache Airflow
- Azure Machine Learning
- Exploring machine learning platforms
- Discussing attributes to consider in a pipeline platform
- Creating your model-building pipeline
- Obtaining training and testing datasets
- Creating a PNG parser
- PNG image files with embedded executables
- Investigating feature engineering
- Training and Building Production Models
- Section 4: Extending ML.NET
- Summary
- Utilizing a database
- Logging
- Single-download optimization
- Additional ideas for improvements
- Running the browser application
- Running the trainer application
- The Program class
- The ProgramArguments class
- Diving into the trainer application
- MainPage.xaml.cs
- MainPage.xaml
- The MainPageViewModel class
- Diving into the UWP browser application
- The WebContentTrainer class
- The WebContentPredictor class
- The WebContentFeatureExtractor class
- The WebPagePredictionItem class
- The WebPageInputItem class
- The ExtensionMethods class
- The Converters class
- The WebPageResponseItem class
- The Constants class
- Diving into the library
- Exploring the project architecture
- Creating the web browser classification application
- View Models
- Models
- Views
- Breaking down the UWP architecture
- Using ML.NET with UWP
- Summary
- Utilizing a database
- Utilizing a caching layer
- Logging
- Exploring additional ideas for improvements
- Running the web application
- Running the trainer application
- The Program class
- The ProgramActions enumeration
- The ProgramArguments class
- Diving into the trainer application
- The Index.razor file
- The Startup class
- The UploadController class
- Diving into the web application
- The FileClassificationTrainer class
- The FileClassificationPredictor class
- The FileClassificationFeatureExtractor class
- The HashingExtensions class
- The ExtensionMethods class
- The Converters class
- The FileDataPrediction class
- The FileData class
- The FileClassificationResponseItem class
- Diving into the library
- Exploring the project architecture
- Creating the file classification web application
- Blazor
- Views
- Models
- Controllers
- Understanding the ASP.NET Core architecture
- Breaking down ASP.NET Core
- Using ML.NET with ASP.NET Core
- Summary
- Utilizing a database
- Utilizing Reflection further
- Logging
- Exploring additional production application enhancements
- Running the application
- The Program class
- The ProgramArguments class
- The Trainer class
- The Predictor class
- The StockPrices class
- The StockPrediction class
- The BaseML class
- The CommandLineParser class
- The ProgramActions enumeration
- Diving into the code
- Exploring the project architecture
- Creating the stock price estimator application
- .NET Core future
- .NET Core targets
- .NET Core architecture
- Breaking down the .NET Core application architecture
- Using ML.NET with .NET Core and Forecasting
- Section 3: Real-World Integrations with ML.NET
- Summary
- RMSE
- R-squared
- MAE
- MSE
- Loss function
- Evaluating a matrix factorization model
- Running the application
- The Constants class
- The Trainer class
- The Predictor class
- The MusicPrediction class
- The MusicRating class
- Diving into the code
- Exploring the project architecture
- Creating a matrix factorization application
- Diving into the matrix factorization trainer
- Use cases for matrix factorizations
- Breaking down matrix factorizations
- Matrix Factorization Model
- Summary
- Detection rate at false positive count
- Area under the ROC curve
- Evaluating a randomized PCA model
- Running the application
- The Trainer class
- The Predictor class
- The LoginPrediction class
- The LoginHistory class
- The Constants class
- Diving into the code
- Exploring the project architecture
- Creating an anomaly detection application
- Running the application
- The Program class
- The Trainer class
- The Predictor class
- The NetworkTrafficPrediction class
- The NetworkTrafficHistory class
- Diving into the code
- Exploring the project architecture
- Creating a time series application
- Diving into time series transforms
- Diving into the randomized PCA trainer
- Use cases for anomaly detection
- Breaking down anomaly detection
- Anomaly Detection Model
- Summary
- Normalized mutual information
- The Davies-Bouldin Index
- Average distance
- Evaluating a k-means model
- Running the application
- The Program class
- The Trainer class
- The Predictor class
- The FeatureExtractor class
- The FileTypePrediction class
- The FileData class
- The FileTypes enumeration
- The BaseML class
- The Constants class
- Diving into the code
- Exploring the project architecture
- Creating the clustering application
- Diving into the k-means trainer
- Use cases for clustering
- Breaking down the k-means algorithm
- Clustering Model
- Summary
- Log-Loss Reduction
- Log Loss
- Macro Accuracy
- Micro Accuracy
- Area Under Precision-Recall Curve
- F1 Score
- Area Under ROC Curve
- Accuracy
- Evaluating a classification model
- Running the application
- The Trainer class
- The Predictor class
- The EmailPrediction class
- The Email class
- Diving into the code
- Exploring the project architecture
- Diving into the trainer
- Creating a multi-class classification application
- Running the application
- The Program class
- The Trainer class
- The Predictor class
- The CarInventoryPrediction class
- The CarInventory class
- Diving into the code
- Exploring the project architecture
- Diving into the trainer
- Creating a binary classification application
- Choosing a classification trainer
- Breaking down classification models
- Classification Model
- Summary
- Root mean squared error
- R-squared
- Mean absolute error
- Mean squared error
- Loss function
- Evaluating a regression model
- Running the application
- The Program class
- The Trainer class
- The Predictor class
- The BaseML class
- The FilePrediction class
- The FileInput class
- The FeatureExtractor class
- Diving into the code
- Exploring the project architecture
- Creating the logistic regression application
- Running the application
- The Program class
- The Trainer class
- The Predictor class
- The EmploymentHistoryPrediction class
- The EmploymentHistory class
- The ExtensionMethods class
- Diving into the code
- Exploring the project architecture
- Diving into the trainer
- Creating the linear regression application
- Choosing a logistic regression trainer
- Choosing a linear regression trainer
- Choosing the type of regression model
- Breaking down regression models
- Regression Model
- Section 2: ML.NET Models
- Summary
- Evaluating the model
- Running the example
- The Program class
- The BaseML class
- The Predictor class
- The Trainer class
- The RestaurantPrediction class
- The RestaurantFeedback class
- Running the code
- Project architecture
- Creating the project in Visual Studio
- Creating your first ML.NET application
- Creating a process
- Installing .NET Core 3
- Installing Visual Studio
- Setting up your development environment
- Setting Up the ML.NET Environment
- Summary
- Extensibility of ML.NET
- Components of ML.NET
- Technical details of ML.NET
- What is ML.NET?
- Matrix factorization
- Clustering
- Anomaly detection
- Regression
- Binary classification
- Exploring various machine learning algorithms
- Unsupervised learning
- Supervised learning
- Exploring types of learning
- Model evaluation
- Model training
- Feature extraction and pipeline
- Obtaining a dataset
- Defining your features
- Defining your problem statement
- The model building process
- The importance of learning about machine learning today
- Getting Started with Machine Learning and ML.NET
- Section 1: Fundamentals of Machine Learning and ML.NET
- Reviews
- Get in touch
- Conventions used
- Download the color images
- Download the example code files
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is searching for authors like you
- About the reviewer
- About the author
- Contributors
- Why subscribe?
- About Packt
- Dedication
- Hands-On Machine Learning with ML.NET
- Copyright and Credits
- Title Page
- 封面
- 封面
- Title Page
- Copyright and Credits
- Hands-On Machine Learning with ML.NET
- Dedication
- About Packt
- Why subscribe?
- Contributors
- About the author
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Section 1: Fundamentals of Machine Learning and ML.NET
- Getting Started with Machine Learning and ML.NET
- The importance of learning about machine learning today
- The model building process
- Defining your problem statement
- Defining your features
- Obtaining a dataset
- Feature extraction and pipeline
- Model training
- Model evaluation
- Exploring types of learning
- Supervised learning
- Unsupervised learning
- Exploring various machine learning algorithms
- Binary classification
- Regression
- Anomaly detection
- Clustering
- Matrix factorization
- What is ML.NET?
- Technical details of ML.NET
- Components of ML.NET
- Extensibility of ML.NET
- Summary
- Setting Up the ML.NET Environment
- Setting up your development environment
- Installing Visual Studio
- Installing .NET Core 3
- Creating a process
- Creating your first ML.NET application
- Creating the project in Visual Studio
- Project architecture
- Running the code
- The RestaurantFeedback class
- The RestaurantPrediction class
- The Trainer class
- The Predictor class
- The BaseML class
- The Program class
- Running the example
- Evaluating the model
- Summary
- Section 2: ML.NET Models
- Regression Model
- Breaking down regression models
- Choosing the type of regression model
- Choosing a linear regression trainer
- Choosing a logistic regression trainer
- Creating the linear regression application
- Diving into the trainer
- Exploring the project architecture
- Diving into the code
- The ExtensionMethods class
- The EmploymentHistory class
- The EmploymentHistoryPrediction class
- The Predictor class
- The Trainer class
- The Program class
- Running the application
- Creating the logistic regression application
- Exploring the project architecture
- Diving into the code
- The FeatureExtractor class
- The FileInput class
- The FilePrediction class
- The BaseML class
- The Predictor class
- The Trainer class
- The Program class
- Running the application
- Evaluating a regression model
- Loss function
- Mean squared error
- Mean absolute error
- R-squared
- Root mean squared error
- Summary
- Classification Model
- Breaking down classification models
- Choosing a classification trainer
- Creating a binary classification application
- Diving into the trainer
- Exploring the project architecture
- Diving into the code
- The CarInventory class
- The CarInventoryPrediction class
- The Predictor class
- The Trainer class
- The Program class
- Running the application
- Creating a multi-class classification application
- Diving into the trainer
- Exploring the project architecture
- Diving into the code
- The Email class
- The EmailPrediction class
- The Predictor class
- The Trainer class
- Running the application
- Evaluating a classification model
- Accuracy
- Area Under ROC Curve
- F1 Score
- Area Under Precision-Recall Curve
- Micro Accuracy
- Macro Accuracy
- Log Loss
- Log-Loss Reduction
- Summary
- Clustering Model
- Breaking down the k-means algorithm
- Use cases for clustering
- Diving into the k-means trainer
- Creating the clustering application
- Exploring the project architecture
- Diving into the code
- The Constants class
- The BaseML class
- The FileTypes enumeration
- The FileData class
- The FileTypePrediction class
- The FeatureExtractor class
- The Predictor class
- The Trainer class
- The Program class
- Running the application
- Evaluating a k-means model
- Average distance
- The Davies-Bouldin Index
- Normalized mutual information
- Summary
- Anomaly Detection Model
- Breaking down anomaly detection
- Use cases for anomaly detection
- Diving into the randomized PCA trainer
- Diving into time series transforms
- Creating a time series application
- Exploring the project architecture
- Diving into the code
- The NetworkTrafficHistory class
- The NetworkTrafficPrediction class
- The Predictor class
- The Trainer class
- The Program class
- Running the application
- Creating an anomaly detection application
- Exploring the project architecture
- Diving into the code
- The Constants class
- The LoginHistory class
- The LoginPrediction class
- The Predictor class
- The Trainer class
- Running the application
- Evaluating a randomized PCA model
- Area under the ROC curve
- Detection rate at false positive count
- Summary
- Matrix Factorization Model
- Breaking down matrix factorizations
- Use cases for matrix factorizations
- Diving into the matrix factorization trainer
- Creating a matrix factorization application
- Exploring the project architecture
- Diving into the code
- The MusicRating class
- The MusicPrediction class
- The Predictor class
- The Trainer class
- The Constants class
- Running the application
- Evaluating a matrix factorization model
- Loss function
- MSE
- MAE
- R-squared
- RMSE
- Summary
- Section 3: Real-World Integrations with ML.NET
- Using ML.NET with .NET Core and Forecasting
- Breaking down the .NET Core application architecture
- .NET Core architecture
- .NET Core targets
- .NET Core future
- Creating the stock price estimator application
- Exploring the project architecture
- Diving into the code
- The ProgramActions enumeration
- The CommandLineParser class
- The BaseML class
- The StockPrediction class
- The StockPrices class
- The Predictor class
- The Trainer class
- The ProgramArguments class
- The Program class
- Running the application
- Exploring additional production application enhancements
- Logging
- Utilizing Reflection further
- Utilizing a database
- Summary
- Using ML.NET with ASP.NET Core
- Breaking down ASP.NET Core
- Understanding the ASP.NET Core architecture
- Controllers
- Models
- Views
- Blazor
- Creating the file classification web application
- Exploring the project architecture
- Diving into the library
- The FileClassificationResponseItem class
- The FileData class
- The FileDataPrediction class
- The Converters class
- The ExtensionMethods class
- The HashingExtensions class
- The FileClassificationFeatureExtractor class
- The FileClassificationPredictor class
- The FileClassificationTrainer class
- Diving into the web application
- The UploadController class
- The Startup class
- The Index.razor file
- Diving into the trainer application
- The ProgramArguments class
- The ProgramActions enumeration
- The Program class
- Running the trainer application
- Running the web application
- Exploring additional ideas for improvements
- Logging
- Utilizing a caching layer
- Utilizing a database
- Summary
- Using ML.NET with UWP
- Breaking down the UWP architecture
- Views
- Models
- View Models
- Creating the web browser classification application
- Exploring the project architecture
- Diving into the library
- The Constants class
- The WebPageResponseItem class
- The Converters class
- The ExtensionMethods class
- The WebPageInputItem class
- The WebPagePredictionItem class
- The WebContentFeatureExtractor class
- The WebContentPredictor class
- The WebContentTrainer class
- Diving into the UWP browser application
- The MainPageViewModel class
- MainPage.xaml
- MainPage.xaml.cs
- Diving into the trainer application
- The ProgramArguments class
- The Program class
- Running the trainer application
- Running the browser application
- Additional ideas for improvements
- Single-download optimization
- Logging
- Utilizing a database
- Summary
- Section 4: Extending ML.NET
- Training and Building Production Models
- Investigating feature engineering
- PNG image files with embedded executables
- Creating a PNG parser
- Obtaining training and testing datasets
- Creating your model-building pipeline
- Discussing attributes to consider in a pipeline platform
- Exploring machine learning platforms
- Azure Machine Learning
- Apache Airflow
- Apache Spark
- Summary
- Using TensorFlow with ML.NET
- Breaking down Google's Inception model
- Creating the WPF image classification application
- Exploring the project architecture
- Diving into the WPF image classification application
- The MainWindowViewModel class
- The MainWindow.xaml class
- The MainWindow.xaml.cs file
- The BaseML class
- The ImageDataInputItem class
- The ImageDataPredictionItem class
- The ImageClassificationPredictor class
- Running the image classification application
- Additional ideas for improvements
- Self-training based on the end user's input
- Logging
- Utilizing a database
- Summary
- Using ONNX with ML.NET
- Breaking down ONNX and YOLO
- Introducing ONNX
- The YOLO ONNX model
- Creating the ONNX object detection application
- Exploring the project architecture
- Diving into the code
- The DimensionsBase class
- The YoloBoundingBox class
- The MainWindow.xaml file
- The ImageClassificationPredictor class
- The MainWindowViewModel class
- Running the application
- Exploring additional production application enhancements
- Logging
- Image scaling
- Utilizing the full YOLO model
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-24 16:44:15
