Deep Learning
Showing 36 of 937 projects
A quantization extension for Keras that provides drop-in replacement layers for creating quantized deep learning models in TensorFlow.
A simplified Keras-like framework for PyTorch that reduces boilerplate code for training neural networks.
TensorFlow implementation of R-Net for machine reading comprehension on the SQuAD dataset.
A convolutional neural network model for real-time road-object segmentation from 3D LiDAR point clouds.
A Python library for generating high-quality synthetic tabular data using GANs, diffusion models, and large language models.
A TensorFlow-based image recognition system for captchas that works without image segmentation.
A Go library implementing feed-forward and Elman recurrent neural networks for machine learning tasks.
A command-line tool for neural network inference using Unix pipeline philosophy.
Autograd automatically differentiates native Torch code, enabling automatic gradient computation for machine learning models.
A Go library implementing feedforward/backpropagation neural networks with support for multiple activation functions, solvers, and classification modes.
A web application that uses a CNN model to recognize handwritten Chinese characters from an online drawing canvas.
A curated list of popular deep learning models for image classification, segmentation, and detection with key performance metrics.
A U-Net implementation for brain tumor segmentation using the BRATS 2017 dataset with data augmentation and dice loss.
A unified deep learning and reinforcement learning framework supporting multiple backends and hardware platforms.
A lightweight encoder-decoder neural network for real-time semantic segmentation on resource-constrained devices.
A reading comprehension dataset with Wikipedia summaries, full stories, and question-answer pairs for narrative understanding.
A sliding window framework for classifying high-resolution whole-slide microscopy and histopathology images using deep neural networks.
A standalone reimplementation of TensorFlow for Ruby, supporting pure Ruby and OpenCL backends for machine learning.
A fast C++ GPU implementation of Convolutional Neural Networks with multi-GPU support.
A collection of BERT-like transformer models pre-trained on chemical SMILES data for drug design and property prediction.
A deep learning framework for detecting and localizing upper-body, lower-body, and full-body clothes in fashion images.
A Caffe implementation of MTCNN for joint face detection and alignment using a multi-task cascaded convolutional neural network.
A TensorFlow implementation of the neural style transfer algorithm that applies artistic styles to images.
An open-source benchmark toolkit for Natural Language Generation in spoken dialogue systems, featuring multiple RNN-based models and datasets.
A real-time dashboard for monitoring Keras model training and evaluation metrics in your browser.
A Torch implementation of a VIS+LSTM model for answering questions about images using deep learning.
A Python library for fast, reproducible, and modular Neural Architecture Search (NAS) to generate efficient deep networks.
A PyTorch implementation of TResNet, a high-performance convolutional neural network architecture optimized for GPU training and inference.
A deep learning library for single-cell analysis of biological images, specializing in cell segmentation and tracking.
A deep learning toolkit for predicting regulatory activity, 3D genome folding, and mRNA half-life from DNA/RNA sequences.
A deep learning framework for integrating single-cell multi-omics data using graph-linked unified embeddings.
A parallel deep learning framework written in modern Fortran for training and inference of dense, convolutional, and transformer networks.
A real-time, uncertainty-aware deep learning model for semantic segmentation of 3D LiDAR point clouds in autonomous driving.
An open-source machine learning solution for the Home Credit Default Risk Kaggle competition, providing reproducible code and experiments.
A fast Clojure library for tensor operations and deep learning with optimized CPU/GPU support.
A JavaScript library and workspace for building and experimenting with dynamic neural network architectures.
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