Neural Networks
Showing 36 of 382 projects
A Torch7 package providing extended neural network modules, criterions, and utilities for deep learning research.
Integrates Intel OpenVINO with ROS 2 for efficient deep learning inference in computer vision applications on Intel hardware.
A TensorFlow implementation of neural text generation from structured data, converting tabular information into natural language summaries.
A Python package providing gradient-based optimizers specifically designed for machine learning scenarios.
Node.js bindings for the Fast Artificial Neural Network Library (FANN), enabling neural network development in JavaScript.
A Torch-based deep learning project for breaking CAPTCHA systems using CNN and RNN architectures.
A deep learning model using generative adversarial networks for fast compressed sensing MRI reconstruction.
A Keras implementation of Microsoft's R-NET neural network for question answering on the SQuAD dataset.
A collection of Jupyter notebooks demonstrating TensorFlow Lite model quantization, conversion, and optimization techniques for deep neural networks.
A simple and powerful neural network library for Python with a MATLAB-like API and flexible configuration.
A Swift library for programmatically creating and exporting CoreML models using a Swift-native DSL and API.
A neuroevolution-based trading bot that evolves populations of neural networks to trade cryptocurrency using technical analysis.
A high-performance tensor library for the V programming language, providing n-dimensional data structures and linear algebra operations.
A Python library that builds neural networks with minimal boilerplate code for PyTorch and TensorFlow.
Unofficial JAX/Flax implementations of deep learning research papers for vision transformers and other architectures.
An open-source CAD framework for designing, simulating, and deploying deep neural networks on embedded platforms.
A curated archive of research papers and resources on generative modeling, covering GANs, image synthesis, 3D generation, and applications.
A pure, immutable module system for JAX that replaces PyTorch-style imperative coding with declarative parameter trees.
A simple machine learning framework written in Swift, currently focusing on regression algorithms.
.NET Standard bindings for Apache MXNet, providing C# developers with NumPy-compatible APIs for machine learning model development, training, and deployment.
A Python toolkit for optimizing chemical reactions using machine learning strategies and benchmarks.
A type-safe, functional ONNX API and backend for deep learning and classical machine learning in Scala 3.
A TensorFlow implementation of hierarchical attentive recurrent neural networks for single object tracking in videos.
A PyTorch implementation of the DeepDream algorithm for generating psychedelic, dream-like images from neural network activations.
A blockchain-based protocol for trustless evaluation and purchase of machine learning models on Ethereum.
A fast, flexible, and compact deep learning framework for Julia that runs on CPU and CUDA GPU.
A TensorFlow-based neural network model for generating descriptive captions from images using Flickr30K and MSCOCO datasets.
A Clojure wrapper for the Encog machine learning framework, specializing in neural network construction and training.
A compact spiking neural network library built on JAX and Haiku, offering high-performance training via surrogate gradient descent and neuroevolution.
A JVM library providing the lowest barrier of entry to image processing, computer vision, and neural networks using OpenCV.
A hands-on workshop introducing deep learning concepts with practical examples using neural networks, CNNs, RNNs, and autoencoders.
A tool for cell instance aware segmentation in densely packed 3D volumetric images, originally developed for plant tissues.
Flax implementations and pretrained checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX.
A Julia library providing a consistent API for common machine learning algorithms, designed for practitioners working with in-memory datasets.
A machine learning and optimization framework for Objective-C and Swift, focused on regression and multi-objective evolutionary algorithms.
A deep belief net and deep learning implementation written in F# with GPU acceleration via Alea.cuBase.
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