The Top 99 Dropout Open Source Projects on Github

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The Top 99 Dropout Open Source Projects on Github

Categories > Machine Learning > Dropout

Pytorch Tutorial ⭐ 5,823

Build your neural network easy and fast, 莫烦Python中文教学

Tensorflow Tutorial ⭐ 3,873

Tensorflow tutorial from basic to hard

Satania.moe ⭐ 488

Satania IS the BEST waifu, no really, she is, if you don't believe me, this website will convince you

Dropblock ⭐ 408

Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.

Theano_lstm ⭐ 309

🔬 Nano size Theano LSTM module

Deepnet ⭐ 267

Implementations of CNNs, RNNs and deep learning techniques in pure Numpy

Targeted Dropout ⭐ 254

Complementary code for the Targeted Dropout paper

Tensorflow Mnist Cnn ⭐ 171

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

Densenet Sdr ⭐ 151

repo that holds code for improving on dropout using Stochastic Delta Rule

Ailearnnotes ⭐ 122

Artificial Intelligence Learning Notes.

Lstms.pth ⭐ 110

PyTorch implementations of LSTM Variants (Dropout + Layer Norm)

Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).

Daguan_2019_rank9 ⭐ 85

达观信息提取比赛第九名代码

AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.

Dropblock Pytorch ⭐ 64

Implementation of DropBlock in Pytorch

Cplxmodule ⭐ 63

Complex-valued neural networks for pytorch and Variational Dropout for real and complex layers.

Dropouts ⭐ 57

PyTorch Implementations of Dropout Variants

Deep_gcn_benchmarking ⭐ 56

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Deep Learning 101 ⭐ 56

The tools and syntax you need to code neural networks from day one.

Cifar10cnnflask ⭐ 55

Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.

Machine Learning In Python Workshop ⭐ 54

My workshop on machine learning using python language to implement different algorithms

Numpy Neuralnet Exercise ⭐ 42

Implementation of key concepts of neuralnetwork via numpy

Svhn Cnn ⭐ 40

Google Street View House Number(SVHN) Dataset, and classifying them through CNN

Variance Networks ⭐ 37

Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019

Variational_dropout ⭐ 37

Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch

Bayesian Neural Network in PyTorch

Imputation method for scRNA-seq based on low-rank approximation

Lol Match Prediction ⭐ 24

Win probability predictions for League of Legends matches using neural networks

Keras Drop Block ⭐ 23

DropBlock implemented in Keras

Dropclass_speaker ⭐ 17

DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020

Variational Dropout ⭐ 15

TensorFlow implementation of the method from Variational Dropout Sparsifies Deep Neural Networks, Molchanov et al. (2017)

Curriculum Dropout ⭐ 13

Code for the paper "Curriculum Dropout", ICCV 2017

My solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition

Nips Global Paper Implementation Challenge ⭐ 12

Selective Classification For Deep Neural Networks.

A pytorch implementation of MCDO(Monte-Carlo Dropout methods)

Dropout_tutorial_in_pytorch ⭐ 11

Dropout as Regularization and Bayesian Approximation

Randomly ⭐ 11

A Library for Denoising Single-Cell Data with Random Matrix Theory

Neural Networks And Deep Learning ⭐ 9

Deep learning projects including applications (face recognition, neural style transfer, autonomous driving, sign language reading, music generation, translation, speech recognition and NLP) and theories (CNNs, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, hyperparameter tuning, regularization, optimization, Residual Networks). Deep Learning Specialization by Andrew Ng, deeplearning.ai

Neuralnetwork ⭐ 9

Neural Network implementation in Numpy and Keras. Batch Normalization, Dropout, L2 Regularization and Optimizers

Deepexperiments ⭐ 9

TensorFlow/Keras experiments on computer vision and natural language processing

Coursera Ng Improving Deep Neural Networks Hyperparameter Tuning Regularization And Optimization ⭐ 9

Short description for quick search

Visual Explanation using Uncertainty based Class Activation Maps

Keras Targeted Dropout ⭐ 8

Targeted dropout implemented in Keras

Indoor Positioning ⭐ 8

Drophead Pytorch ⭐ 7

An implementation of drophead regularization for pytorch transformers

Deep Learning ⭐ 7

Implemented the deep learning techniques using Google Tensorflow that cover deep neural networks with a fully connected network using SGD and ReLUs; Regularization with a multi-layer neural network using ReLUs, L2-regularization, and dropout, to prevent overfitting; Convolutional Neural Networks (CNNs) with learning rate decay and dropout; and Recurrent Neural Networks (RNNs) for text and sequences with Long Short-Term Memory (LSTM) networks.

Trafficsignclassifier ⭐ 7

This project is an aspect of a big project that is called the Self-Driving Car. One of the essential techniques in Self-Driving Car engineering is detecting the Traffic Sign. In this project I have used Deep Learning for recognizing the Traffic Signs.

Dropblock Caffe ⭐ 6

Implementation of DropBlock: A regularization method for convolutional networks in Caffe.

Adaptative Dropout Pytorch ⭐ 6

Pytorch implementation of Adaptative Dropout a.ka Standout.

Advanceddropout ⭐ 6

Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization (IEEE TPAMI 2021)

Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition

Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality.

Deeplearning_from_scratch ⭐ 6

A Deep Learning framework for CNNs and LSTMs from scratch, using NumPy.

Cap6619 Deep Learning Term Project ⭐ 5

Term project for CAP6619 Deep Learning class, Fall 2018 - dropout vs. batch normalization

Realtime Hand Gesture Recognition ⭐ 5

Hand Gesture Recognition using CNNs and Perceptrons in realtime (OpenCV)

Numpy implementation of deep learning

Neural Network Handwritten Digit Classification ⭐ 5

Two hidden layer Neural Network with 99% Accuracy. Dropout Regularization scheme is also implemented and available as an option. Please read the report for full implemantation Description.

Keras Drop Connect ⭐ 5

Drop-connect wrapper

Multilabel Image Classification Using Contractive Autoencoder ⭐ 5

Implementing contractive auto encoder for encoding cloud images and using that encoding for multi label image classification

Weed Detection ⭐ 4

This Problem is based on a Image Data set consisting of different types of weeds, to detect them in crops and fields. I have used Deep Learning Model called CNN(Convolutional Neural Networks) with Dropout, Batch Normalization, ReduceLearning rate on plateau, Early stoppig rounds, and Transposd Convolutional Neural Networks.

Deeperthought ⭐ 4

Simple GPGPU CUDA C++ code for training Deep Neural Networks (DNN)

An OOP Deep Neural Network using a similar syntax as Keras with many hyper-parameters, optimizers and activation functions available.

Svhn Deep Neural Network ⭐ 3

Implementing an Image classification neural network to classify Street House View Numbers

A Simple dropout implementation in C++

Kerastroke ⭐ 3

A suite of the generalization-improvement techniques Stroke, Pruning, and NeuroPlast

Bayesian Convolutional Neural Network Crack Detection ⭐ 2

A Bayesian Convolutional Neural Network approach for image-based crack detection and maintenance applications

Plantleafdiseasedetection ⭐ 2

Deep learning using CNN in tensorflow on Kaggle image dataset containing 87,900 different healthy and unhealthy crop leaves spanning 38 unique classes.

Credit Card Frauid Detection Using Deep Learning ⭐ 2

The objective of this project is to identify the fraudulent transactions happening in E-Commerce industry using deep learning.

V2 Plant Seedlings Classification ⭐ 2

It has 12 Classes for twelve different types of crops. This Dataset is in a zip file containing twelve folders of each plant containing their pictures. It is a Image Classification Problem, which can be easily solved Deep Learning Models such CNN(Convolutional Neural Networks)

Machine Learning From Scratch Codingchallenge ⭐ 2

Implement Linear Regression, Logistic Regression, Neural networks and many other

Customized Caffe for pseudo-random dropout

Tensorflow In Practice ⭐ 2

TensorFlow in Practice Specialization. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

Dropin Esn ⭐ 2

This repository contains the code used to produce the results presented in the IJCNN 2017 paper "DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout" by D. Bacciu, F. Crecchi (University of Pisa) and D. Morelli (Biobeats LTD).

Kuzushiji Dropblock ⭐ 2

Japanese Handwritten Character Recognition using DropBlock Regulzarization

Sparseout ⭐ 2

Sparseout: Controlling Sparsity in Deep Networks

RZiMM: A Regularized Zero-inflated Mixture Model for scRNA-seq Data

Digitally recognizing numbers in real life images has been a tough problem in artificial intelligence for many decades. The problem stems from the seemingly endless variations on fonts, colors, spacings, locations etc that these numbers can take within an image.

Neural Networks ⭐ 1

Real-world application sized Neural Network. Implemented back-propagation algorithm with momentum, auto-encoder network, dropout during learning, least mean squares algorithm.

Cats Vs Dogs Classification Computer Vision ⭐ 1

Cats vs dogs classification using deep learning. Data augmentation and convolutional neural networks.

Neurorepository ⭐ 1

Фреймворк для построения нейронных сетей, комитетов, создания агентов с параллельными вычислениями.

Pytorch Stochastic Depth ⭐ 1

Deep Networks with Stochastic Depth for PyTorch

Classifying_traffic_signs ⭐ 1

Identify traffic sign images through Supervised Classification via Deep Learning and Computer Vision using Python, Tensorflow, Jupyter and Anaconda in AWS Cloud.

Dl Assignment2 Cs231n ⭐ 1

My solution to the 2nd assignment of UdelaR's Deep Learning course, based on Stanford CS231n.

Fraternal Nmt ⭐ 1

Neural Machine Translation with Fraternal Dropout

Convolutional_nn ⭐ 1

Implemented fully-connected DNN of arbitrary depth with Batch Norm and Dropout, three-layer ConvNet with Spatial Batch Norm in NumPy. The update rules used for training are SGD, SGD+Momentum, RMSProp and Adam. Implemented three block ResNet in PyTorch, with 10 epochs of training achieves 73.60% accuracy on test set.

Effect Of Dropout On Model Performance Toward Skin Cancer Classification ⭐ 1

Convnet_mnist ⭐ 1

Simple convolutional neural network (purely numpy) to classify the original MNIST dataset. My first project with a convnet. 🖼

Deeplearning.ai Specialization ⭐ 1

5 courses of Specialization in Deep Learning taught by Prof.Andrew Ng on Coursera

Fractional_max_pooling_and_recurrent_convolutional_neural_network ⭐ 1

Implementation of fractional max pooling model and recurrent convolutional neural network

Ships In Satellite Images ⭐ 1

Satellite imagery provides unique insights into various markets, including agriculture, defense and intelligence, energy, and finance. New commercial imagery providers, such as Planet, are using constellations of small satellites to capture images of the entire Earth every day. This flood of new imagery is outgrowing the ability for organizations to manually look at each image that gets captured, and there is a need for machine learning and computer vision algorithms to help automate the analysis process. The aim is to help address the difficult task of detecting the location of large ships in satellite images. Automating this process can be applied to many issues including monitoring port activity levels and supply chain analysis.

Random Pixel Drop ⭐ 1

Regularization Collection Deeplearning ⭐ 1

This is Collection of Regularization Deep learning techniques with code and paper

Tensorflow Doc Summary ⭐ 1

Tensorflow를 이용하여 영어 요약문을 학습 시키는 프로그램

Mva_bml_dropoutuncertainty ⭐ 1

Project for the course Bayesian Machine Learning (MVA)

Deeplearningpracticalworks ⭐ 1

Neural networks and deep learning practical works

Human Activity Recognition ⭐ 1

Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions.

Uncertainty Segmentation ⭐ 0

Uncertainty quantification for semantic image segmentation models using Monte Carlo dropout method

1-99 of 99 projects

Advertising 📦 9

Application Programming Interfaces 📦 120

Applications 📦 181

Artificial Intelligence 📦 72

Blockchain 📦 70

Build Tools 📦 111

Cloud Computing 📦 79

Code Quality 📦 28

Collaboration 📦 30

Command Line Interface 📦 48

Community 📦 81

Companies 📦 60

Compilers 📦 60

Computer Science 📦 74

Configuration Management 📦 39

Content Management 📦 167

Control Flow 📦 197

Data Formats 📦 77

Data Processing 📦 266

Data Storage 📦 132

Economics 📦 60

Frameworks 📦 198

Graphics 📦 103

Hardware 📦 148

Integrated Development Environments 📦 47

Learning Resources 📦 147

Libraries 📦 119

Lists Of Projects 📦 21

Machine Learning 📦 336

Mapping 📦 61

Marketing 📦 15

Mathematics 📦 55

Messaging 📦 97

Networking 📦 304

Operating Systems 📦 84

Operations 📦 120

Package Managers 📦 52

Programming Languages 📦 229

Runtime Environments 📦 96

Science 📦 42

Security 📦 375

Social Media 📦 26

Software Architecture 📦 70

Software Development 📦 68

Software Performance 📦 57

Software Quality 📦 127

Text Editors 📦 45

Text Processing 📦 131

User Interface 📦 310

User Interface Components 📦 465

Version Control 📦 29

Virtualization 📦 68

Web Browsers 📦 38

Web Servers 📦 25

Web User Interface 📦 194

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