I know there's a rule against self-promotion, but I am hoping my work will actually be very useful for C# and .NET lovers, who also want to get into machine learning. TL;DR; Over the past 2 years I've made a .NET binding to the full TensorFlow Python API, including Keras, tf.contrib, and, basically, everything else. It's called Gradient, its on NuGet, and you can read the guide here: https://github.com/losttech/Gradient/#getting-started It started with the desire to explore deep learning, where I quickly discovered you basically have to use Python for the "latest and greatest" frameworks and SotA. And I don't like dynamic languages, and love C#. There was CNTK, which worked nicely, but never gained enough community. It was (and still is) very hard to find advanced sample code for it. It would take enormous effort to manually port TensorFlow in its entirety to .NET. Projects like TensorFlowSharp and TensorFlow.NET are trying to get to that stat
When working on your AI project , if you have to handle a large collection of rich media, such as images, video or audio, traditional machine learning algorithms are not going to be enough. In this case, you need a deep learning framework. Deep Learning (DL) frameworks are basically libraries , interfaces, and tools that help you build deep learning models more easily. They have pre-built components that are optimized so you don’t have to build underlying algorithms. However, choosing a deep learning framework is not an easy task. In this article, I’ll provide an overview of the top solutions to help you select a framework for your project. What is a Deep Learning Framework and how to choose it? To understand this concept, let’s use an example. Consider the images in the picture below. While these are all animals, there are several categories in this image – Owl, Horse, Lion, Giraffe, and so on. If we need to classify these images into their corresponding categories, a