53 Open Source Dropout Software Projects
Free and open source dropout code projects including engines, APIs, generators, and tools.
Satania.moe 530 ⭐
Satania IS the BEST waifu, no really, she is, if you don't believe me, this website will convince you
Dropblock 517 ⭐
Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
Parasdahal Deepnet 291 ⭐
Implementation of CNNs, RNNs, and many deep learning techniques in plain Numpy.
Tensorflow Mnist Cnn 187 ⭐
MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.
Thtrieu Essence 69 ⭐
AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.
Icellr 94 ⭐
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)).
Cifar10cnnflask 62 ⭐
Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.
Machine Learning In Python Workshop 74 ⭐
My workshop on machine learning using python language to implement different algorithms
Variational_dropout 43 ⭐
Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch
Variance Networks 37 ⭐
Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019
Cplxmodule 77 ⭐
Complex-valued neural networks for pytorch and Variational Dropout for real and complex layers.
Bayesian Active Learning Pytorch 31 ⭐
Implementation of Bayesian NNs in Pytorch (https://arxiv.org/pdf/1703.02910.pdf) (With some help from https://github.com/Riashat/Deep-Bayesian-Active-Learning/))
Understanding Neuralnetworks Pytorch 26 ⭐
Understanding nuts and bolts of neural networks with PyTorch
Lol Match Prediction 29 ⭐
Win probability predictions for League of Legends matches using neural networks
Dropclass_speaker 19 ⭐
DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020
Variational Dropout 16 ⭐
TensorFlow implementation of the method from Variational Dropout Sparsifies Deep Neural Networks, Molchanov et al. (2017)
Srinadhu Cs231n 29 ⭐
My solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition
Georgezoto Tensorflow In Practice 44 ⭐
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
Coursera Ng Improving Deep Neural Networks Hyperparameter Tuning Regularization And Optimization 13 ⭐
Short description for quick search
Neural Networks And Deep Learning 20 ⭐
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
Adalbertocq Neuralnetwork 12 ⭐
Neural Network implementation in Numpy and Keras. Batch Normalization, Dropout, L2 Regularization and Optimizers
Deep_gcn_benchmarking 63 ⭐
[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
Psistats 10 ⭐
Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality.
Realtime Hand Gesture Recognition 11 ⭐
Hand Gesture Recognition using CNNs and Perceptrons in realtime (OpenCV)
Ttungl Deep Learning 10 ⭐
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 11 ⭐
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.
Weed Detection 11 ⭐
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.