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AI and machine learning are huge topics in technology. In this series, we'll explore how Haskell's unique features as a language can be valuable in crafting better AI programs. In particular, we'll explore some advanced concepts in type safety, and apply these to the machine learning framework Tensor Flow. Haskell has strong abstraction capabilities and built-in memory management. Its code is highly reusable and easy to understand. This language is favored by many developers because it increases productivity.
Haskell’s learning curve, however, is fairly steep when compared to other languages commonly used for AI development. Haskell is a lazy language, which means that values are evaluated only when needed. This often leads to difficulties in memory allocation.
One of the most popular Haskell libraries for machine learning is HLearn. The library exploits the algebraic structures inherent in learning systems and contains several useful templates for implementation. Haskell also has a TensorFlow binding which can be used for deep learning.
Haskell is perfect for abstract mathematics and creates highly expressive algorithms while maintaining great performance. Considering its key features, Haskell is good for projects that involve the creation of extensive neural networks. In spite of its obvious benefits though, it does not have the same level of support as other programming languages, making it hard to get support. However, it is notable that Facebook uses a Haskell ML model to fight spamming.
Why Haskell AI/ML Is Important to Your Business
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