Convolutional Neural Networks

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.

Learning Convolutional Neural Networks with Interactive Visualization.

Pytorch implementation of convolutional neural network visualization techniques

Implementation of Graph Convolutional Networks in TensorFlow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Convolutional Neural Network for Text Classification in Tensorflow

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

Graph Convolutional Networks in PyTorch

Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

Sequence modeling benchmarks and temporal convolutional networks

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

Deformable Convolutional Networks

Differentiable architecture search for convolutional and recurrent networks

Deep Convolutional Generative Adversarial Networks

Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

Image Deblurring using Generative Adversarial Networks

Convolutional recurrent network in pytorch

R-CNN: Regions with Convolutional Neural Network Features

Paper Lists for Graph Neural Networks

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

Convolutional Neural Networks to predict the aesthetic and technical quality of images.

Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

Submanifold sparse convolutional networks

Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition.