File-Based Apps in .NET 10: C# Without the Ceremony
Build a URL uptime checker in a single C# file with .NET 10 file-based apps. No project or solution needed, plus NuGet packages.
Jon Wood
Tutorials and ideas.
How to Handle Big Data Files in C# with Parquet.NET
Read and write Parquet files in C# with Parquet.NET, and see why Parquet beats CSV for large datasets. Includes a complete example.
How the Machine Learning Process is Like Cooking
How the machine learning process, from data to a deployed model, mirrors cooking a meal, and why following the process leads to better models.
ML.NET Predictions on the Web in F# with the SAFE Stack
Build an F# web app with the SAFE Stack and serve ML.NET predictions from it, using F# on both the server and the client.
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How the Machine Learning Process is Like Cooking
How the machine learning process, from data to a deployed model, mirrors cooking a meal, and why following the process leads to better models.
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What's New in the Model Builder Preview
A walkthrough of the new features in the ML.NET Model Builder preview, following the full flow from loading data to a trained model.
How to Build the ML.NET Repository
Step-by-step guide to cloning and building the ML.NET repository locally so you can start contributing bug fixes and features.
Perform Linear Regression in Azure Databricks with MLLib
Train and evaluate a linear regression model with Spark MLlib in Azure Databricks, using the UCI Computer Hardware dataset.
ML.NET Predictions on the Web in F# with the SAFE Stack
Build an F# web app with the SAFE Stack and serve ML.NET predictions from it, using F# on both the server and the client.
Participate in the 2020 Virtual ML.NET Hackathon
Join the 2020 Virtual ML.NET Hackathon: a free workshop on ML.NET basics, then a week to build or contribute to an open-source ML project.
Top 5 YouTube Channels
Five YouTube channels I check regularly to learn new development topics, starting with Traversy Media.
Top 5 Machine Learning Books
The five machine learning books I recommend, from Hands-On Machine Learning to Deep Learning with Python, and what each one covers.