Why I downloaded massive anaconda binaries in the past is beyond me to be honest. I like to use virtual environments, so almost none of the stuff in the base anaconda installation is

Azure Machine Learning Service is getting better every day. The newly added Automated ML feature in the workspace portal sure is a great addition that you should definitely check out. Here's how it works.

Want to get your hands dirty with Microsoft CNTK and deep learning? Packt is running a great deal on my book, you can now get it for just $10.

Trying to train a neural  network to classify images is a challenge. You need a large amount of data and compute power to train models from scratch. Customers that I've worked for typically

In the past machine learning was something exclusively for the pythonistas or data scientists that knew how to use Python or R and had studied statistics. Not anymore, it has become a lot easier for developers to use machine learning. This also goes for .NET developers.

When working on machine learning problems I find that productivity is sometimes somewhat low, because I have to spend a lot of time working on tedious bits that probably can be automated away. Over time I have found quite a few tools that help me get better results faster. Here's three of them.

Microsoft now offers a new MCSA certification for people that want to expand into the world of machine learning. Here's a run down of the exams and what you will need to learn to earn this certification.

How do you start with AI when you're a developer and have no idea where to begin? Organize a workshop for your colleagues and challenge them to start exploring machine learning. I certainly learned a ton while organizing the AI learning experience. It was a lot of fun.

I've embarked on my hardest and most fun adventure ever. I'm going to implement my own deep learning framework. I know most people will call me crazy. But I think this is a challenge that was long coming and I finally got around to it. Wonder why? Read on.

Discover how you can use Cognitive Toolkit to bring your models to production with Python and Java.

When you're building complex neural networks you often want to visualize them. Visualization offers a great way to debug things like performance bottlenecks, because parts of your network get duplicated by CNTK. Luckely, it is quite easy to visualize a neural network in CNTK. Here's how.

Microsoft cognitive toolkit supports loading and saving models in ONNX format, which enables you as a developer to run your trained network on Java and C# with much better overall performance than you can get with Python.

You can train any model you like for your AI solution, but it is only as good as the data you feed it. But how do you measure the performance of a machine learning model? In this third part of the series on AI models I will show you how.