Search results

Layer - Neural network inference from the command line, implemented in.

Project that automatically maps relationship networks by parsing public data using LLMs and visualizes it as an interactive graph.

Blog for understanding Neural Networks!

Neural networks, regression and feature learning in Clojure.

FANN (Fast Artificial Neural Network) binding.

Course materials and notes for Stanford class CS231n: Deep Learning for Computer Vision.

Torch CUDA Neural Network Implementation.

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.

Deeplearn-rs provides simple networks that use matrix multiplication, addition, and ReLU under the MIT license.

Deep learning in Rust, with shape checked tensors and neural networks.

FeatherCNN is a high performance inference engine for convolutional neural networks.

Deep Neural Networks for Golang (powered by MXNet).

An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.

A beginner-friendly guide on using Keras to implement a simple Neural Network in Python.

Simple API for Neural Network. Better for image processing with CPU/GPU + Transfer Learning.

Lightweight library to build and train neural networks in Theano.

Neural network inference from the command line, implemented in CHICKEN Scheme.

A graph sampling extension library for NetworkX with a Scikit-Learn like API.

A header-only C++11 Neural Network library. Low dependency, native traditional chinese document.

Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch.

A low-code framework for building custom AI models like LLMs and other deep neural networks. opensource.

Neural networks (boltzmann machines, feed-forward and recurrent nets), Gaussian Processes.

It implemented multi-perceptrons neural network (ニューラルネットワーク) based on Back Propagation Neural Networks (BPN) and designed unlimited-hidden-layers.