#Dropout
Showing 18 of 18 repositories tagged #dropout, ranked by stars
Build your neural network easy and fast, 莫烦Python中文教学
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
:microscope: Nano size Theano LSTM module
Artificial Intelligence Learning Notes.
repo that holds code for improving on dropout using Stochastic Delta Rule
A Deep Learning and preprocessing framework in Rust with support for CPU and GPU.
Bayesian Neural Network in PyTorch
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
Google Street View House Number(SVHN) Dataset, and classifying them through CNN
Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.
Win probability predictions for League of Legends matches using neural networks
Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch
Deep learning using CNN in tensorflow on Kaggle image dataset containing 87,900 different healthy and unhealthy crop leaves spanning 38 unique classes.
Implementation of key concepts of neuralnetwork via numpy
A Library for Denoising Single-Cell Data with Random Matrix Theory
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/))
Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.