Deep Learning
Showing 36 of 937 projects
A lightweight deep learning library written in C++ with C, C#, and Python interfaces, supporting CPU and GPU computation.
A TensorFlow wrapper library for character-level and word-level text generation using recurrent neural networks.
A GitHub Action that retrieves model runs and metrics from Weights & Biases for integration into CI/CD workflows.
A curated list of academic papers, datasets, and metrics for 3D neurite segmentation in electron microscopy connectomics.
A PyTorch-based project for classifying chest X-rays and localizing pathologies using Grad-CAM with fine-tuned CNN models.
A curated dataset of 1,044,394 Windows executable samples with frequency-domain image representations for malware detection research.
A high-level neural networks API for Delphi, written in Pascal with Python binding and support for TensorFlow, CNTK, or Theano backends.
A TensorFlow implementation of convolutional highway networks for deep learning experiments.
A multi-view fusion network for aerial 3D human pose and shape estimation using autonomous UAVs with on-board RGB cameras.
A TensorFlow library implementing Restricted Boltzmann Machines and their variants for machine learning applications.
A lightweight, high-performance C++ wrapper for TensorFlow that simplifies development with a modern API.
A Swift deep learning library for iOS and macOS with Accelerate and Metal support.
Go binding for the MXNet C Predict API to perform inference with pre-trained deep learning models.
An interpretable multi-task deep neural network for single-cell multi-omics integration and cross-modal analysis.
A deep learning model that translates between single-cell multi-omic profiles, such as scATAC-seq and scRNA-seq, using a shared latent representation.
An open-source pipeline for high-throughput, non-invasive extraction of phenotypic traits from plant images using deep learning.
An easy-to-use PyTorch library for faster computer vision model development and training.
A PyTorch-based framework providing implementations of state-of-the-art deep learning models for computer vision tasks.
V language bindings for Mozilla's DeepSpeech, enabling speech-to-text functionality in V applications.
Deploy a neural network model that transfers artistic styles from one image to another using a ResNet-based architecture.
A lightweight, header-only C++11 deep neural network library designed for embedded systems with minimal dependencies.
Ruby bindings for the Apache MXNet deep learning framework, enabling Ruby developers to build and train neural networks.
A PyTorch framework for reinforcement learning research, focused on reproducibility and fast experimentation.
An open-source solution for the Google AI Open Images Object Detection Challenge, providing a RetinaNet-based benchmark with experiment tracking.
A 3D convolutional network software package for extracting axonal and filamentous structures from cleared brain imaging data.
A Ruby deep learning library supporting fully connected, convolutional, and recurrent neural networks.
A collection of neural network models ported from torchvision for use with JAX and Flax.
Code for the CIFAR-10 Kaggle competition using cuda-convnet for image classification.
A library of reusable web components for data annotation tasks in computer vision applications.
A Python module for creating convolutional autoencoders to model background error covariance in variational data assimilation.
A collection of demos and tutorials for learning Torch7, a scientific computing framework with deep learning support.
Estimates horizon lines from single images using deep learning models trained on diverse outdoor scenes.
Sample code demonstrating Core ML integration with ResNet50 and custom models converted via coremltools.
TensorFlow implementation of AlexNet with 3D convolutional layers for volumetric image recognition.
A pre-trained image classifier that recognizes 365 different scene and location types using a ResNet18 model fine-tuned on Places365.
Auralisation tool for visualizing learned features in convolutional neural networks applied to audio spectrograms.
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