Backpropagation
Showing 27 of 27 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.
A Julia package implementing backpropagation artificial neural networks for machine learning tasks.
A neural network library in Go where neurons and synapses are implemented as goroutines.
A Julia implementation of a backpropagation neural network for machine learning tasks.
A Python library for building and training feedforward neural networks with GPU support and mini-batch learning.
A 3-layer neural network library for iOS implementing Back Propagation Neural Network (BPN) with QuickProp and Kecman's theory.
A V programming language module for creating and training multi-layer neural networks with backpropagation.
A Crystal shard providing artificial intelligence algorithms, including neural networks, ported from the AI4R library.
An iOS library implementing multi-layer perceptron neural networks with backpropagation for machine learning tasks.
An embedded deep learning library for Go designed for educational exploration of neural network fundamentals.
A monadic implementation of fully-connected neural networks in OCaml with backpropagation and customizable hyperparameters.
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