VGG16 architecture with BatchNorm

I implemented a classifier using batchnorm provided by tensorflow slim and analyzed the results using tensorboard.

remove batchnorm when deploy mxnet model

MNIST Classification using Neural Network and Back Propagation. Written in Python and depends only on Numpy

fuse batch normalization layer and con layer in CNN

MXNet Gluon Synchronized Batch Normalization Preview

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

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). …

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers https://arxiv.org/abs/1802.00124

Synchronized BatchNorm in PyTorch 1.0

Built MLP with ReLU and Adam optimization with 2 layers, 3 layers and 5 layers and observed how it works.

How to use Cross Replica / Synchronized Batchnorm in Pytorch

Built CNN model with different no. of layers with added dropout on MNIST data

This repository contains different implementation of deep learning model using Numpy.

A Re-implementation of Fixed-update Initialization

Neural network with momentum and batchnorm (MNIST classification example)

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

Review materials for the TWiML Study Group. Contains annotated versions of the original Jupyter noteboooks (look for names like *_jcat.ipynb ), slide decks from weekly Zoom meetups, etc.

Digit recognition neural network using the MNIST dataset. Features include a full gui, convolution, pooling, momentum, nesterov momentum, RMSProp, batch normalization, and deep networks.

A C# WGAN.