Browser-based visual editor for designing neural networks and automatically generating PyTorch and TensorFlow code.
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The iOS and OS X neural network framework.
Theano based library for deep and recurrent neural networks.
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!
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.
Deep learning in Rust, with shape checked tensors and neural networks.
FeatherCNN is a high performance inference engine for convolutional neural networks.
Implementation of image to image (pix2pix) translation from the paper by isola et al.[DEEP LEARNING].
An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.
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.
CEA-List's CAD framework for designing and simulating Deep Neural Network, and building full DNN-based applications on embedded platforms.
Ncnn is a high-performance neural network inference framework optimized for the mobile platform.
Visualizer for neural network, deep learning and machine learning models.
Analyzes structured data and helps produce queries, insights or visualizations.
Helps find, analyze or synthesize information for research and knowledge discovery.
Named-entity recognition using neural networks providing state-of-the-art-results.