Jon Wood has another great video on ML.NET, this time focusing on Tokenizing Text Data as part of an NLP.
The new DatabaseLoader in ML.NET was updated in the 1.4-preview version.
Jon Wood made this video to show you how to use it.
Jon Wood has created another video showing how to use ML.NET and the (currently) preview version of 1.4 to create a deep neural network model to classify images.
Jon Wood has another great video on how to save and load binary data in your ML.NET applications to build models against.
Binary data can give a performance boost when loading.
Jon Wood has another great video teaching you how to how to use ML.NET to perform transfer leaning from the Inception model built in Tensorflow.
ML.NET allows .NET developers to easily build and also consume machine learning models in their NET applications.
In this episode, Bri Achtman joins Rich to show off some really interesting scenarios that ML.NET and its family of tools enables. They talk about training models, AutoML, the ML.NET CLI, and even a Visual Studio Extension for training models!
Machine Learning is red hot right now and entering the field can be daunting for newcomers.
There are new languages, tooling, and frameworks to learn on top of very deep mathematical concepts.
Here’s a great article on ML.NET and how it brings familiar programming languages and techniques into the tools developers use every day.
That’s where ML.Net comes in. It’s Microsoft’s open source, cross-platform machine learning tool for .Net and .Net Core, targeting .Net Standard, running on Windows, Mac OS, and Linux systems. It’s extensible, so it works with not only Microsoft’s own ML tooling but also with other frameworks such as Google’s TensorFlow and the ONNX cross-platform model export technology. By supporting as wide a selection of frameworks as possible, it gives you the option to pick and choose the ML models that are closest to your needs, fine tuning them to fit.
ML.NET, Microsoft’s open source machine learning framework, has been updated to version 1.2. Here’s a quick rundown of the updates. Read the article on Visual Studio Magazine to find out more.
- General availability of TimeSeries support for forecasting and anomaly detection:
- General availability of ML.NET packages to use TensorFlow and ONNX models:
- Easily integrate ML.NET models in web or serverless apps with Microsoft.Extensions.ML integration package (preview):
- ML.NET CLI updated to 0.14 (preview):
- Model Builder updates:
Expanding support to .txt files and more delimiters for values
No limits on training data size
Smart defaults for training time for large datasets
Improved model consumption experience
ML.NET is a free, cross-platform and open source machine learning framework designed to bring the power of machine learning (ML) into .NET applications.
Live from Build 2019, we are joined by Cesar De La Torre Llorente who gives us a great overview of what the goals of ML.NET are, and shares with us some of the highlights of the 1.0 release.