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Announcing .NET Machine Learning Notebook Series
.NET Interactive Notebooks .NET Interactive takes the power of .NET and embeds it into interactive experiences. One of these interactive experiences is notebooks. Notebooks are files that contain executable code, visualizations, and raw text all within the same file. Interactive notebooks are used extensively in data science and machine learning. They are great for data exploration and preparation, experimentation, model explainability, and education. Since the announcement of the Notebook Editor extension in Visual Studio last October, we've been constantly working to improve the experience. We've continuously made stability and performance improvements to the extension and in our most recent release we enabled keyboard shortcuts and deep-linking capabilities to automatically open notebooks in Visual Studio. To go along with these updates and showcase the power of notebooks for data science and machine learning applications, we've put together a series of notebooks that cover getting started and end-to-end machine learning scenarios. This is the first version of these notebooks and we plan to continue iterating on them. How can you get started? On a recent episode of VS Toolbox Live, we provided an overview of notebooks in Visual Studio and the machine learning notebook series. Take a look at that recording to get a sense of what notebooks are and how they can be useful for your workflows. When you're ready to get started exploring the notebooks yourself: Download and install the latest version of Visual Studio. Download and install the latest version of the Visual Studio Notebook Editor extension. Download or clone the dotnet/csharp-notebooks GitHub repository and open the machine-learning directory in Visual Studio. Alternatively if you're on Mac or Linux, use Visual Studio Code and the .NET Interactive Notebooks extension. We want to hear from you Let us know about your experiences: Provide suggestions on notebook topics in the dotnet/csharp-notebooks GitHub repository. Issues with the .NET Interactive extensions? File an issue in the dotnet/interactive GitHub repository.Train Machine Learning Models with ML.NET Model Builder on ARM64
Visual Studio 2022 17.3 Preview 2 is now available as a native ARM64 application on Windows 11. This is the first version of Visual Studio that natively supports building and debugging ARM64 apps on ARM-based processors. .NET workloads are one of the prioritized workloads. What is Model Builder? Model Builder is a Visual Studio extension that uses ML.NET, an open-source, cross-platform machine learning framework for .NET developers to train and deploy custom machine learning models into .NET apps. Get started To get started with Model Builder on ARM64 devices: Install ARM64 Visual Studio Enable Model Builder Note: For image classification scenarios, we recommend using Azure since it's not supported for local environments on ARM devices. For more information, see the tutorial on how to train an image classification model in Azure using Model Builder. We want to hear from you Let us know about your experiences. If you have any feedback or issues running Model Builder on ARM, file an issue in the dotnet/machinelearning-modelbuilder GitHub repository.Welcome to the Machine Learning and AI .NET space!
Hello and welcome to the Machine Learning & AI space! Here are some resources you might find useful: ML.NET Documentation: https://docs.microsoft.com/dotnet/machine-learning Samples: dotnet/machinelearning-samples: Samples for ML.NET, an open source and cross-platform machine learning framework for .NET. (github.com) Repository: dotnet/machinelearning: ML.NET is an open source and cross-platform machine learning framework for .NET. (github.com) Model Builder Repository: dotnet/machinelearning-modelbuilder: Simple UI tool to build custom machine learning models. (github.com) Machine Learning Community Standup (filter for Machine Learning in dropdown): .NET Community Standups | .NET Live TV (microsoft.com) .NET for Apache Spark Documentation: http://docs.microsoft.com/dotnet/spark Repository: dotnet/spark: .NET for Apache® Spark™ makes Apache Spark™ easily accessible to .NET developers. (github.com) Tools .NET Interactive Notebooks: dotnet/interactive: .NET Interactive takes the power of .NET and embeds it into your interactive experiences. Share code, explore data, write, and learn across your apps in ways you couldn't before. (github.com) .NET Interactive Notebooks VS Code extension: .NET Interactive Notebooks - Visual Studio Marketplace Visual Studio Notebook Editor extension: Notebook Editor - Visual Studio Marketplace Numerical & Statistical Libraries Math.NET Numerics: Math.NET Numerics (mathdotnet.com) FSharp.Stats: FSharp.Stats (fslab.org) Plotting / Graphic Libraries Plotly.NET: Plotly.NET Deep Learning Libraries TensorFlow.NET: SciSharp/TensorFlow.NET: .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#. (github.com) TensorFlow.Keras: NuGet Gallery | TensorFlow.Keras 0.6.4 TorchSharp: dotnet/TorchSharp: .NET bindings for the Pytorch engine (github.com) DiffSharp: DiffSharp: Differentiable Tensor Programming Made Simple OnnxRuntime: microsoft/onnxruntime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator (github.com)Machine Learning Community Standup | May 11, 2022
Topic: Office Hours YouTube Recording Community Links Normalization - Machine Learning Glossary (ML.NET) Introducing Channel Suggestions (Kliptok) Can ML.NET Save Dr. Who? Probably not, but let's give it a go! ( YouTube) Deep Learning with ML.NET (YouTube) Sound Classification using ML.NET - Practical ML.NET User Group 04/14/2022 (YouTube) Serverless Deep Neural Network(DNN) with Azure Functions and ML.Net (YouTube) Feedback We want to hear from you. Do you have topics you'd like to hear more about or have ideas on how we can improve the show? Take a few minutes to fill out our feedback form. Stay Connected Join the Virtual ML.NET Community Discord and the #machine-learning channel on the .NET Development Discord. Find recordings from previous shows on .NET Live TV.Machine Learning Community Standup - January 18, 2023
Topic: New Year, New Releases Recording Community Links Announcements Announcing ML.NET 2.0 Accelerate ML.NET Training with Intel OneDAL Videos .NET Conf 2022 - Announcing ML.NET 2.0 .NET Conf 2022 - Machine Learning Models with ONNX and .NET .NET Conf 2022 - Deep Learning in .NET Getting Practical with ML.NET Series Pt. 1 - Getting Started with ML.NET Getting Practical with ML.NET Series Pt. 2 - Text Classification with Model Builder Getting Practical with ML.NET Series Pt. 3 - Deploying & Consuming ML Models YOLO v7 .NET sample Jon Wood - What's new in ML.NET 2.0 Livestream - How well can ChatGPT write ML.NET Code? Document Extraction Using ML Blog Posts ODSC: Getting Started with ML.NET New Docs / Updates How to use ML.NET AutoML API Deep Learning Overview Razor Pages Sentiment Analysis Deploy to Azure Functions New Samples / Updates ML.NET 2.0 Samples DirectML ONNX Sample Resources ML.NET Website ML.NET Documentation Machine Learning Notebooks Machine Learning Samples ML.NET Case Studies Feedback We want to hear from you. Do you have topics you'd like to hear more about or have ideas on how we can improve the show? Take a few minutes to fill out our feedback form. Stay Connected Join the Virtual ML.NET Community Discord and the #machine-learning channel on the .NET Development Discord. Find recordings from previous shows on .NET Live TVMachine Learning Community Standup | June 22, 2022
Topic: Text Classification, AutoML, and Notebooks Recording Community Links Introducing the ML.NET Text Classification API What's new with ML.NET Automated ML (AutoML) and tooling Train Machine Learning Models with ML.NET Model Builder on ARM64 Announcing .NET Machine Learning Notebook Series C# Notebooks Visual Studio Toolbox Live - Notebooks in Visual Studio Plotly.NET - C# Bindings Progress (Help Wanted) ML.NET Excel Add-In Creating Microsoft .NET MAUI Machine Learning App (ML.NET) MVVM Pattern Part 1 Creating Microsoft .NET MAUI Machine Learning App (ML.NET) MVVM Pattern Part 2 ML.NET Full Stack Course Feedback We want to hear from you. Do you have topics you'd like to hear more about or have ideas on how we can improve the show? Take a few minutes to fill out our feedback form. Stay Connected Join the Virtual ML.NET Community Discord and the #machine-learning channel on the .NET Development Discord. Find recordings from previous shows on .NET Live TVConsume Azure Custom Vision ONNX Models with ML.NET
Azure Custom Vision is an image recognition service that lets you build, deploy, and improve your own computer vision models. Custom Vision allows you to specify your own labels and train custom machine models using your data. Custom Vision supports training models for the following tasks: Image classification Object Detection Once you've trained a model, Custom Vision provides you with a variety of formats to export and deploy your model to. One of those formats is the Open Neural Network Exchange (ONNX). ONNX is an open-source format to represent AI models. ONNX models can be used to make predictions with the help of the ONNX Runtime (ORT). The ORT is a runtime for ONNX models which provides an interface for accelerating the consumption / inferencing of machine learning models, integrating with hardware-specific libraries, and sharing models across programming languages and frameworks like PyTorch, Tensorflow / Keras, scikit-learn, Windows ML, ML.NET, and others. ML.NET is an open-source, cross-platform machine learning framework for .NET developers. ML.NET provides a set of APIs that build on the ONNX Runtime. That means you can take models you've exported from Custom Vision and use them inside your .NET applications. Get started with Custom Vision and ML.NET To get started using Custom Vision models with ML.NET Train an image classification or object detection model. Export your model to ONNX Use and reference these sample applications which show how to build an ML.NET pipeline that consumes a Custom Vision ONNX model. Resources What is Custom Vision? ONNX website ONNX Runtime website What is ML.NET?Machine Learning Community Standup | August 17, 2022
Topic: Introducing SynapseML Recording Community Links .NET Data Hub Machine Learning with ML.NET for Absolute Beginners Plotly C# Bindings NER Announcing SynapseML for .NET Synapse ML Synapse ML GitHub Repo Paper: Large-Scale Intelligent Microservices Mosaic: find artistic connections with deep learning Resources ML.NET Website ML.NET Documentation Machine Learning Notebooks Machine Learning Samples Feedback We want to hear from you. Do you have topics you'd like to hear more about or have ideas on how we can improve the show? Take a few minutes to fill out our feedback form. Stay Connected Join the Virtual ML.NET Community Discord and the #machine-learning channel on the .NET Development Discord. Find recordings from previous shows on .NET Live TV
