ML.NET Archives | Cesar de la Torre

Using ML.NET in Jupyter notebooks

Cesar De la TorreNovember 6, 2019Nov 6, 201911/6/19

I do believe this is great news for the ML.NET community and .NET in general. You can now run .NET code (C# / F#) in Jupyter notebooks and therefore run ML.NET code in it as well! - Under the covers, this is enabled by 'dotnet-try' and its related .NET kernel for Jupyter (as early previews). The Jupyter Notebook is an open-source web ap

Training Image Classification/Recognition models based on Deep Learning & Transfer Learning with ML.NET

Cesar De la TorreSeptember 6, 2019Sep 6, 201909/6/19

Blog Post updated targeting ML.NET 1.4 GA (Nov. 2019) Note that this blog post was updated on Nov. 6th 2019 so it covers the updates provided in ML.NET 1.4 GA, such as Image classifier training and inference using GPU and a simplified API. Context and background for 'Image Classification', 'training vs. scoring' an

Run with ML.NET C# code a TensorFlow model exported from Azure Cognitive Services Custom Vision

Cesar De la TorreJune 5, 2019Jun 5, 201906/5/19

With ML.NET and related NuGet packages for TensorFlow you can currently do the following: However, in the scenario where you want to train with your own images, the Transfer Learning approach can be a bit complex because even without taking into account the code implementation for transfer learning you'll need to find a base TensorF

What is ML.NET 1.0 – Machine Learning for .NET

Cesar De la TorreMay 6, 2019May 6, 201905/6/19

Today, coinciding with //BUILD 2019/ conference, we’re thrilled by launching ML.NET 1.0 release! You can read the official ML.NET 1.0 release announcement Blog Post here and get started at the ML.NET site here. In this blog post I'm providing quite a few additional technical details along with my personal vision that you might find in

How to optimize and run ML.NET models on scalable ASP.NET Core WebAPIs or web apps

Cesar De la TorreMarch 24, 2019Mar 24, 201903/24/19

Context ------ UPDATE on May 13th 2019: The recommended way to deploy/run an ML.NET model into ASP.NET Core web apps or WebAPI services is by using the 'Microsoft.Extensions.ML' Integration package. Read about it in this tutorial: - Deploy an ML.NET model in an ASP.NET Core Web API The tutorial above uses optimized code based on a

Cesar de la Torre

ML.NET Archives | Cesar de la Torre

Using ML.NET in Jupyter notebooks

Training Image Classification/Recognition models based on Deep Learning & Transfer Learning with ML.NET

Run with ML.NET C# code a TensorFlow model exported from Azure Cognitive Services Custom Vision

What is ML.NET 1.0 – Machine Learning for .NET

How to optimize and run ML.NET models on scalable ASP.NET Core WebAPIs or web apps

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