Vulpes
A deep belief net and deep learning implementation written in F# with GPU acceleration via Alea.cuBase.
What is Vulpes?
Vulpes is a deep belief network and deep learning implementation written in F# that uses Alea.cuBase for GPU acceleration. It provides a functional programming approach to neural network training, specifically designed for tasks like image classification on datasets such as MNIST. The project combines unsupervised pretraining with fine-tuning via backpropagation to build accurate models.
F# developers and .NET programmers interested in machine learning who want to leverage functional programming paradigms for neural network development. Researchers or practitioners looking for GPU-accelerated deep learning tools within the .NET ecosystem.
Developers choose Vulpes for its unique combination of F#'s functional programming strengths with GPU-accelerated deep learning, offering an alternative to Python-based frameworks. It provides a specialized implementation of deep belief networks with ready-to-use examples for common machine learning tasks.
Overview
Vulpes: a Deep Belief Net written in F#, and using Alea.cuBase to access the GPU.
Use Cases
Best For
- Implementing deep belief networks in functional programming languages
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