Backpropagation
Showing 17 of 17 projects
Automatically differentiate native Python and NumPy code for gradient-based optimization and machine learning.
A minimal, well-tested library for training and using feedforward artificial neural networks in ANSI C.
A deep learning library in Rust featuring shape-checked tensors and neural networks with compile-time safety.
A deep learning library for Rust featuring shape-checked tensors and neural networks with compile-time safety.
A Java deep learning framework implementing neural networks with GPU acceleration via OpenCL and Aparapi.
A Common Lisp machine learning library focusing on neural networks, Boltzmann machines, and Gaussian processes with BLAS and CUDA support.
A Go library implementing feed-forward and Elman recurrent neural networks for machine learning tasks.
A Go library implementing feedforward/backpropagation neural networks with support for multiple activation functions, solvers, and classification modes.
A purely functional artificial neural network library for Haskell, enabling rapid prototyping through higher-order function composition.
A Go implementation of neural networks including BackPropagation, RBF, and Perceptron networks with parallel processing capabilities.
A feedforward neural network library for Rust implementing backpropagation training.
A lightweight feedforward neural network with resilient backpropagation (Rprop), implemented in pure Ruby with no external dependencies.
A small Clojure library for constructing and training neural networks using core.matrix.
A minimal pure Python implementation of reverse-mode automatic differentiation (autograd) for educational purposes.
A Clojure library for building and training neural networks with support for various architectures and learning algorithms.
A Go module implementing multi-layer neural networks for machine learning tasks.
A multilayer perceptron neural network implementation in Go with backpropagation training.
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