Deep Learning
Showing 36 of 402 projects
An open-source deep learning API and server written in C++ that supports multiple backends like PyTorch, TensorRT, and TensorFlow for training and inference.
Automatic neural architecture search and hyperparameter optimization for PyTorch, focusing on tabular data and time series forecasting.
A JAX/Flax-based framework for easy and scalable pre-training, fine-tuning, evaluation, and serving of large language models.
A Go library that simplifies TensorFlow's Go bindings with method chaining, automatic scoping, and type conversion.
An efficient video and audio loader for deep learning with hardware-accelerated decoding and smart shuffling.
HyperLearn provides 2-2000x faster machine learning algorithms with 50% less memory usage, optimized for all hardware.
A curated list of community detection research papers with implementations.
A ROS package for real-time object detection in camera images using YOLO (V3) on GPU and CPU.
A ROS package for real-time object detection in camera images using YOLO (V3) on GPU and CPU.
A pioneering object detection system that combines region proposals with convolutional neural network features, significantly advancing detection accuracy.
A Python library for graph deep learning built on Keras and TensorFlow 2, providing flexible tools for graph neural networks.
A modular toolkit for machine learning, natural language processing, and text generation with TensorFlow and PyTorch versions.
A curated collection of papers, code, and resources on neural rendering techniques for computer vision and graphics.
A machine learning package implementing message passing neural networks for predicting molecular and reaction properties.
A flow-based generative network for fast, high-quality speech synthesis from mel-spectrograms.
A large-scale dataset of object-centric video clips with 3D bounding box annotations and AR metadata for 3D object detection research.
A Python library for automatic differentiation that generates readable Python source code as its derivative output.
A Python library for audio data augmentation to improve the robustness of audio machine learning models.
A gradient processing and optimization library for JAX, designed for research with composable building blocks.
A gradient processing and optimization library for JAX, designed for research with composable building blocks.
A deep learning system for accurate protein structure and interaction prediction using a three-track neural network.
An open source Python library and framework for building computer vision models on satellite, aerial, and large imagery sets.
A curated list of libraries, tutorials, and resources for implementing machine learning in the Ruby programming language.
A curated list of awesome libraries, data sources, tutorials, and resources for machine learning using the Ruby programming language.
A curated list of datasets, tools, methods, review papers, and competitions for remote sensing change detection.
PyGAD is a Python library for building genetic algorithms and optimizing machine learning models with Keras and PyTorch support.
A simple wrapper that combines Keras and Hyperopt for convenient hyperparameter optimization in deep learning models.
A generalist algorithm for cellular segmentation with human-in-the-loop training and superhuman generalization across diverse microscopy images.
A curated list of academic papers and resources for image and video inpainting techniques.
A curated list of deep learning implementations and resources for biological research, with a focus on genomics.
A curated list of awesome libraries, projects, tutorials, and resources for the JAX machine learning ecosystem.
A Python library for constructing differentiable convex optimization layers in PyTorch, JAX, and MLX using CVXPY.
A web/desktop application for collaborative labeling and annotation of images, text, audio, documents, and other data types.
An open-source library of high-performance, high-quality denoising filters for ray-traced images using deep learning.
A platform for developing AI bots that play Doom using visual information, designed for reinforcement learning research.
An unsupervised learning framework for depth and ego-motion estimation from monocular videos using TensorFlow.
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