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1,209 search results found

Siamesenetwork Tensorflow ⭐ 232

Using siamese network to do dimensionality reduction and similar image retrieval

Np Hard Deep Reinforcement Learning ⭐ 231

pytorch neural combinatorial optimization

Atlas Python ⭐ 230

World's lightest toolkit to quickly and easily add a GUI to your Python programs and bring them online.

Stanford Cs231 ⭐ 229

Resources for students in the Udacity's Machine Learning Engineer Nanodegree to work through Stanford's Convolutional Neural Networks for Visual Recognition course (CS231n).

Deeptextures ⭐ 229

Code to synthesise textures using convolutional neural networks as described in Gatys et al. 2015 (http://arxiv.org/abs/1505.07376)

Spyketorch ⭐ 226

High-speed simulator of convolutional spiking neural networks with at most one spike per neuron.

Sdc Vehicle Detection ⭐ 225

Udacity Project - Vehicle Detection

Deep Declarative Networks

Mixture Density Networks For Distribution And Uncertainty Estimation ⭐ 225

A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)

Overcoming Catastrophic ⭐ 219

Implementation of "Overcoming catastrophic forgetting in neural networks" in Tensorflow

Spytorch ⭐ 218

Tutorial for surrogate gradient learning in spiking neural networks

Link Prediction ⭐ 216

Representation learning for link prediction within social networks

Understanding_tensorflow_nn ⭐ 212

🔮Getting started with TensorFlow: Classifying Text with Neural Networks

Segment-CNN: A Framework for Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs

Bayesian Neural Network Mnist ⭐ 212

Bayesian neural network using Pyro and PyTorch on MNIST dataset

Neural Network From Scratch ⭐ 211

Ever wondered how to code your Neural Network using NumPy, with no frameworks involved?

Structuredinference ⭐ 206

Structured Inference Networks for Nonlinear State Space Models

Pybatfish ⭐ 201

Python client for Batfish: https://github.com/batfish/batfish

Cvpr2019_pyramid Feature Attention Network For Saliency Detection ⭐ 201

code and model of Pyramid Feature Selective Network for Saliency detection

Attention_network_with_keras ⭐ 201

An example attention network with simple dataset.

Graph Convolution Nlp ⭐ 200

Graph Convolution Network for NLP

Deeplearning.ai Note ⭐ 199

网易云课堂终于官方发布了吴恩达经过授权的汉化课程-“”深度学习专项课程“”，这是自己做的一些笔记以及

Applied Deep Learning With Keras ⭐ 195

Deep Learning examples with Keras.

Deep_learning_tutorial ⭐ 192

[ko] 패스트캠퍼스 강의 자료

Spiking Neural Network Snn With Pytorch Where Backpropagation Engenders Stdp ⭐ 192

What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.

Generative_adversarial_networks_101 ⭐ 190

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

Wav2mid ⭐ 186

Automatic Music Transcription with Deep Neural Networks

RadIO is a library for data science research of computed tomography imaging

Activitynet 2016 Cvprw ⭐ 185

Tools to participate in the ActivityNet Challenge 2016 (NIPSW 2016)

Class.vision ⭐ 184

Computer vision and Deep learning

Hyperspectral ⭐ 184

Deep Learning for Land-cover Classification in Hyperspectral Images.

Practical Machine Learning With Python ⭐ 182

Machine Learning Tutorials in Python

Stock Prediction ⭐ 181

Stock price prediction with recurrent neural network. The data is from the Chinese stock.

Code for NeurIPS 2019 paper: "Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes"

Keraspp ⭐ 178

코딩셰프의 3분 딥러닝, 케라스맛

Keras Acgan ⭐ 177

Auxiliary Classifier Generative Adversarial Networks in Keras

Multi-Graph Convolutional Neural Networks

Lstm_networks ⭐ 176

This is the code for "LSTM Networks - The Math of Intelligence (Week 8)" By Siraj Raval on Youtube

Multihead Siamese Nets ⭐ 173

Implementation of Siamese Neural Networks built upon multihead attention mechanism for text semantic similarity task.

Dgc Net ⭐ 167

A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"

Chromagan ⭐ 167

Official Implementation of ChromaGAN: An Adversarial Approach for Picture Colorization

Deepkoopman ⭐ 165

neural networks to learn Koopman eigenfunctions

Deformable Convnets Caffe ⭐ 164

Deformable Convolutional Networks on caffe

Efficient Neural Network Bilibili ⭐ 158

B站Efficient-Neural-Network学习分享的配套代码

FPGA-based neural network inference project with an end-to-end approach (from training to implementation to deployment)

Gate Decorator Pruning ⭐ 156

Code for the NuerIPS'19 paper "Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks"

Deep Q Learning ⭐ 154

Recurrent Spatial Transformer Code ⭐ 152

Code for Recurrent Spatial Transformer Networks

Pydata2015 ⭐ 150

Pytorch Book ⭐ 147

Source codes for the book "Application of Neural Network and PyTorch"

Attentionn ⭐ 147

All about attention in neural networks. Soft attention, attention maps, local and global attention and multi-head attention.

Stanford_cs231n_2019 ⭐ 147

Solutions and comments to assignments for 2019 Stanford's course on convolutional neural networks

Echo State Networks in Python

CS579: Online Social Network Analysis at the Illinois Institute of Technology

D3graph ⭐ 143

Creation of interactive networks using d3 Javascript

Recurrent_neural_network ⭐ 143

This is the code for "Recurrent Neural Networks - The Math of Intelligence (Week 5)" By Siraj Raval on Youtube

Riptide ⭐ 143

Simple Training and Deployment of Fast End-to-End Binary Networks

Episodic Curiosity ⭐ 142

Tensorflow/Keras code and trained models for Episodic Curiosity Through Reachability

Rnn_lstm_from_scratch ⭐ 142

How to build RNNs and LSTMs from scratch with NumPy.

A lightweight network for body/hand action recognition

Graph_convolutional_lstm ⭐ 140

Traffic Graph Convolutional Recurrent Neural Network

Photorealistic Style Transfer ⭐ 139

High-Resolution Network for Photorealistic Style Transfer

Fgsegnet ⭐ 133

FgSegNet: Foreground Segmentation Network, Foreground Segmentation Using Convolutional Neural Networks for Multiscale Feature Encoding

Nn_calibration ⭐ 133

Calibration of Convolutional Neural Networks

Road network extraction from satellite imagery, with speed and travel time estimates

One Network Many Uses ⭐ 130

Four-in-one deep network: image search, image captioning, similar words and similar images using a single model

Datascience_book ⭐ 128

Deep Q Learning ⭐ 127

Tensorflow implementation of Deepminds dqn with double dueling networks

Siamese_net ⭐ 126

This package shows how to train a siamese network using Lasagne and Theano and includes network definitions for state-of-the-art networks including: DeepID, DeepID2, Chopra et. al, and Hani et. al. We also include one pre-trained model using a custom convolutional network.

Deep Steganography ⭐ 125

Hiding Images within other images using Deep Learning

Simplegesturerecognition ⭐ 123

A very simple gesture recognition technique using opencv and python

Fastmri Reproducible Benchmark ⭐ 122

Try several methods for MRI reconstruction on the fastmri dataset. Home to the XPDNet, runner-up of the 2020 fastMRI challenge.

Covid 19 Detection Flask App Based On Chest X Rays And Ct Scans ⭐ 121

COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. A Flask App was later developed wherein user can upload Chest X-rays or CT Scans and get the output of possibility of COVID infection.

Reverse Engineering Neural Networks ⭐ 120

A collection of tools for reverse engineering neural networks.

Tensorflow 2.0 Quick Start Guide ⭐ 117

Tensorflow 2.0 Quick Start Guide, published by Packt

Hierarchical Attention Network ⭐ 117

Implementation of Hierarchical Attention Networks in PyTorch

Dynamic_neural_manifold ⭐ 117

Xup_vitis_network_example ⭐ 117

VNx: Vitis Network Examples

DHGNN source code for IJCAI19 paper: "Dynamic Hypergraph Neural Networks"

Adversarialconsistentscorematching ⭐ 116

Code for paper "Adversarial score matching and improved sampling for image generation"

Graph2nn ⭐ 115

code for paper "Graph Structure of Neural Networks"

Introdatasci ⭐ 114

Course materials for: Introduction to Data Science and Programming

Generative_adversarial_networks_live ⭐ 113

Parametricumap_paper ⭐ 112

Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).

Invertinggradients ⭐ 109

Algorithms to recover input data from their gradient signal through a neural network

Cs231n 2019 Assignments ⭐ 108

Solution to CS231n Assignments 2019

Pytorch Model Zoo ⭐ 108

A collection of deep learning models implemented in PyTorch

Prototypical Networks Tensorflow ⭐ 108

Tensorflow implementation of NIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network

Networks ⭐ 105

Network and Graph Algorithms From Scratch

Autoencoders ⭐ 105

Implementation of simple autoencoders networks with Keras

Advanced Deep Learning With Python ⭐ 104

Advanced Deep Learning with Python

Neural networks from scratch

Siamese Networks ⭐ 103

Few Shot Learning by Siamese Networks, using Keras.

CS224D Assignments

Nmn Pytorch ⭐ 103

Neural Module Network for VQA in Pytorch

Examples ⭐ 103

Fully-working mlpack example programs

Nn Brain ⭐ 102

Tutorial codes for modeling brains with neural nets

Deep Learning Coursera ⭐ 102

Projects from the Deep Learning Specialization from deeplearning.ai provided by Coursera

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