F#’s efficient execution, concise style, data accessibility and scalability make it suitable for mathematical tasks in science and engineering, finance, and data analysis and modeling. And therefore also for machine learning. F# scores with compact program code that is often more efficient than other languages. Programs in F# consist of definitions of types, variables, and functions. The types of these definitions are determined automatically using type inference.
Data types in F# are immutable. This and type inference avoids side effects in the program code. F# supports function composition, pattern matching, recursion, lists and other collections, and higher-order functions. The functional paradigm makes parallel and asynchronous programming simpler than in other languages. F# targets a wide range of use cases and includes many language elements as well as a large standard library.
- With the ML.NET library, F# developers get all the tools they need to build machine learning models. Machine learning can be quickly integrated into web, mobile, desktop, and IoT applications.
- The TorchSharp library, which is based on PyTorch, makes it easy to work with tensors and integrate GPUs via Nvidia’s CUDA API.
- TorchSharp works with dynamic computation graphs so that tasks can be changed at runtime and all gradients can be optimized dynamically. This differentiates TorchSharp from the more static TensorFlow framework.
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Daniel Basler works as a Software Developer at Solarlux GmbH. His focus is on cross-platform apps, Android, JavaScript, and Microsoft technologies.
F# is a full member of the .NET Framework languages ​​and also allows you to extend other .NET Framework applications with F# code. With the help of ML.NET and the TorchSharp library, F# provides the perfect tools for building machine learning models. This article demonstrates how to build an ML model using F# and TorchSharp with a simple example. Previous knowledge of functional programming with F# is an advantage, but not absolutely necessary.
This was a reading sample of our Heise Plus article “Machine Learning: Building ML Models with F# and TorchSharp”. With a Heise Plus subscription you can read and listen to the full article.
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