#Recurrent Neural Network

xLSTM is an extended Recurrent Neural Network architecture that addresses the limitations of traditional LSTMs. Utilizing Exponential Gating and a Matrix Memory design, it offers enhanced performance for language modeling and serves as a notable alternative to both Transformers and State Space Models. Compatible with PyTorch, xLSTM is straightforward to install and configure for diverse applications, with features like xLSTMBlockStack and xLSTMLMModel adaptable for various use cases in language processing without exaggeration in claims.

Grenade is a dependently typed neural network library optimized for Haskell that allows for precise configuration of complex networks. It supports both recurrent and convolutional networks, achieving low error rates on tasks like MNIST. Through its flexible layer structure, Grenade offers support for custom network designs including residual and series parallel graphs. The library is purely functional and efficient, utilizing backpropagation and gradient updates with performance optimized by C-based LAPACK and BLAS libraries.