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This is my presentation at the Edmonton .NET User Group. http://www.edmug.net/
Watch video:
Gitter is like a message board where you can ask questions about Blazor and other people can answer your questions.
(Collection)Jan. 31, 2019 - Presentation - Blazor and ML.NET
Nov 28, 2018 - Steve Sanderson - Blazor, a New .NET Single Page Application Framework
- new Single Page Application Framework
- server: .NET, GO, Java, node.js
- client: JavaScript, Angular, React, Vue
- run other languages
- convert any language to byte code that runs in browser
- support in modern browsers
- Web Assembly been out for two years
Show default Blazor project (using C#)
- no javascript
- cshtml - component
- static markup in HTML, @page (Home page)
- dynamic markup using Razor syntax, @functions C# code (Counter page)
- http service dependency injection, @inject (Weather page)
Blazor Components
- can be nested
- can have events
- can pass parameters
Show EPPPlus demo to export data into Excel spreadsheet
- can use any .NET library/ecosystem
Different type of projects (client vs Full-stack)
- stand alone client app
- ASP.NET core hosted (3 projects - Client, Server, Shared), Full-stack; calls server api to fetch weather service
Show how to compile code to Web Assembly
- emcc compiler
- generate .wasm (web assembly)
- generate .js (wrapper)
- compile mono runtime to .wasm
- script tag > src="_framework/blazor.webassembly.js"
- mono.js
- mono.wasm
- .NET assemblies / dlls (load into browser)
- 1.95 MB download
- performance can be issue for public site; it might be okay for an intranet app
- blazor - UI stack; work on memory data structure, compare difference and update only elements that is different
- blazor client side - experimental
- blazor server side (Razor component) - use web socket connection, 100 KB download, ship in ASP.NET Core 3.0, takes load from client, thin client, fast, run on server, simply architecture; cons: cannot work offline, latency between server and client, using server resources
- blazor desktop (Electron) - Show Azure Storage Explorer example
Compare hosting models:
Other resources:
January 2019 Event
Blazor.net website
The following is the Blazor website.
Getting Started with Blazor
Blazor Flight Finder example
GitHub - ASP.NET - Blazor
Here you can find the latest release and source code for Blazor.
Gitter Community for Blazor
Sep. 28, 2018 - Microsoft Ignite - Machine Learning with ML.NET
These are some notes from the talk at Microsoft Ignite.
Program the unprogrammable
Machine Learning creates a Model using this data
Many ML tasks
github demo label issue
Pre-built machine learning models (Azure Cognitive Services)
Limitations with pre-built machine learning models
Build your own custom machine learning models
pre-built vs custom machine learning models
custom machine learning models
ML.NET is a framework for building custom ML Models
Machine Learning Samples - https://github.com/dotnet/machinelearning-samples
Sentiment Analysis Sample - https://github.com/dotnet/machinelearning-samples/tree/master/samples/csharp/getting-started/BinaryClassification_SentimentAnalysis
sentiment analysis - is it A or B problem
sentiment analysis - feature input and label output.png
Machine learning workflow
Text Featurizer - text to numeric - ngram extraction
important concepts - data, transformer, estimator, prediction
Transformer - n-gram
Estimator - create Model
Prediction - single input -> single output
demo sentiment analysis binary classification
- code
- metrics
- prediction function
demo github multiclassification
More data you have the better results you may get
iterative machine learning
different pipeline
- different user, repository (data)
- different learner
demo movie recommendations
Collaborative filtering approach
prediction label and scores
Nov. 23, 2018 - Ed Charbeneau - Blazor and ML.NET sample project
Watch video:
Author blog:
Download code:
ML.NET
- .NET Standard 2.0.3 reference ML.NET 0.7.0
- 70% training and 30% test data (actual data)
Taxi Fare Sample
- https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/taxi-fare
- Console app - reference Nuget Microsoft.ML package
- features and label (i.e. FareAmount)
- train - read the data, transform the data
- fast tree - regression learner
- evaluation metric for regression model - RSquared (closer to 1 the better the model is)
- evaluation metric for regression model - Rms (the lower it is the better the model)
- Function input (TaxiTrip) => output TaxiTripFarePrediction
Taxi Service
- load model into MLContext
Blazor Server side (Razor Component)
- Blazor Server side is compatible with ML.NET as a dll service.
Blazor client side (web assembly)
- cannot add ML.NET as a reference
- challenge on how to read the model file
- whether blazor will crap out when running the service dll
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