Deep Learning
Showing 36 of 937 projects
A JavaScript library and workspace for building and experimenting with dynamic neural network architectures.
A 3D vision library for monocular and stereo 3D human detection, social distancing, and body orientation estimation from 2D keypoints.
A JavaScript library and workspace for building and experimenting with dynamic neural network architectures.
An Android example project demonstrating TensorFlow integration for handwritten digit recognition using the MNIST dataset.
A benchmark dataset and toolkit for RF-based drone detection and identification using raw IQ data and deep learning models.
A PyTorch library for creating and training autoencoders on sequential data (time series, videos, etc.) in just two lines of code.
A neural network for object detection using multi-level fusion of camera and radar data, built on Keras RetinaNet.
A toolkit that streamlines and automates the generation of model cards for machine learning models.
OCaml bindings for PyTorch, providing NumPy-like tensor computations with GPU acceleration and automatic differentiation.
A 100M-parameter foundation model for single-cell transcriptomics, enabling gene expression enhancement, drug response prediction, and perturbation analysis.
Annotated notes and summaries of the TensorFlow white paper, with SVG figures and links to documentation.
A minimalist GPU-only framework for N-dimensional convolutional neural networks focused on speed and hackability.
A collection of deep learning tutorials and implementations using Keras and Lasagne, with a focus on Theano-based frameworks.
Deep learning models for crop yield prediction using remote sensing data, with CNN/LSTM and Gaussian Process approaches.
A lightweight neural network for near-real-time semantic segmentation of LiDAR point clouds using polar coordinate quantization.
A simple, realtime visualization server for monitoring neural network training performance across frameworks.
A PyTorch reinforcement learning library implementing DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, and IMPALA.
A curated list of deep learning research papers and implementations for high dynamic range image and video synthesis.
An open-source machine learning framework built in Rust for high-performance and extensible ML tasks.
Reference mapping for single-cell genomics
An open-source machine learning framework built in Rust for high-performance and extensible ML tasks.
A vision transformer-based deep learning model for automated instance segmentation and classification of cell nuclei in histopathology images.
Interactive visualization tool for understanding Capsule Network (CapsNet) layers and their internal workings.
A curated list of research, applications, tutorials, and software built using the H2O open-source machine learning platform.
A collection of genomic language models for predicting variant effects and evolutionary constraints from DNA sequences.
Convert Torch7 neural network models to Apple CoreML format for deployment on iOS/macOS devices.
A purely functional artificial neural network library for Haskell, enabling rapid prototyping through higher-order function composition.
A Ruby gem providing TensorFlow bindings for basic tensor operations and machine learning tasks.
A collection of open-source machine learning and quantitative analysis models implemented in TensorFlow and PyTorch.
A fast neural network framework for iOS and macOS using Swift and Metal for GPU acceleration.
A neural network playground built in pure Swift for iOS, with no third-party dependencies.
A neural networks toolbox for medical image analysis, providing specialized layers, models, and utilities for TensorFlow/Keras.
A high-level machine learning library for Go with a Keras-like API, built on Gorgonia.
An easy-to-use C# deep learning library with support for multiple backends including TensorFlow, PyTorch, and CUDA/OpenCL.
A re-implementation of the SPP-net object detection algorithm using spatial pyramid pooling in deep convolutional networks for visual recognition.
A curated list of deep learning resources for 3D shape processing, covering classification, reconstruction, and generation.
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