shainet
Neural Network Library
A neural network implementation using object-oriented modeling and inspired by biological systems
SHAInet - a pure Crystal machine learning library
184 stars
31 watching
19 forks
Language: Crystal
last commit: about 2 years ago
Linked from 1 awesome list
convolutional-neural-networkscrystaldeep-learningdeep-neural-networksmachine-learningneural-network
Related projects:
Repository | Description | Stars |
---|---|---|
| A Crystal binding for the Fast Artificial Neural Network library (FANN) to provide a simple interface for creating and training neural networks. | 85 |
| A small neural network implementation of the backpropagation algorithm in Haskell | 127 |
| A fast neural network library for iOS and Mac OS X with vectorized operations and hardware acceleration. | 900 |
| An implementation of Artificial Neural Networks in Ruby, allowing developers to experiment and train neural networks using the language. | 36 |
| An implementation of a multi-layer neural network in Python, allowing users to train and use the network for classification tasks. | 5 |
| A library that provides an autograd and GPGPU framework for dynamic neural networks in D. | 7 |
| A Python library for implementing and training various neural network architectures | 40 |
| A neural network implementation for machine learning on iOS | 33 |
| A Haskell-based framework for flexible neural networks and similar parameterized models with automatic differentiation and modular training algorithms. | 124 |
| A Ruby-based deep learning library for building and training neural networks | 46 |
| A Python library for building and training neural networks. | 163 |
| A collection of libraries for building and training neural networks in various programming languages | 70 |
| A lightweight, portable C implementation of a feedforward artificial neural network library | 593 |
| A C++ library for building and training neural networks with high-performance support for large datasets. | 275 |
| A compact C++17-based deep learning library designed to simplify the implementation of neural networks. | 16 |