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simple neural network library in ANSI C
The main features of codeplea/genann are: Artificial Intelligence, Machine Learning, Machine Learning and AI, General Libraries, General Purpose Libraries.
Projects with overlapping indexed features include: tensorflow/tensorflow — TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of… ujjwalkarn/machine-learning-tutorials — This repository serves as a structured educational resource for machine learning and data science, providing a… dobiasd/frugally-deep — A lightweight header-only library for using Keras (TensorFlow) models in C++. pytorch/pytorch — PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array… tiny-dnn/tiny-dnn — tiny-dnn is a header-only C++14 deep learning framework for building, training, and running inference on neural… apache/incubator-mxnet — Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying…
PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui
TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr
A lightweight header-only library for using Keras (TensorFlow) models in C++.
tiny-dnn is a header-only C++14 deep learning framework for building, training, and running inference on neural networks. It constructs static computational graphs at compile time using template-based layer composition, with a gradient-based backpropagation engine and minibatch stochastic gradient descent for training, all without external dependencies beyond the C++14 standard library. The framework supports importing pre-trained models from the Caffe framework directly, parsing its binary serialization format without requiring external protocol buffer libraries. It provides CPU-optimized te