There are 4 repositories under batch-normalization topic.
Build your neural network easy and fast, 莫烦Python中文教学
ImageNet pre-trained models with batch normalization for the Caffe framework
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.
Deep learning library in plain Numpy.
Deep Learning Specialization courses by Andrew Ng, deeplearning.ai
Adaptive Affinity Fields for Semantic Segmentation
Synchronized Multi-GPU Batch Normalization
MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.
Batch normalization fusion for PyTorch. This is an archived repository, which is not maintained.
iCellR is an interactive R package designed to facilitate the analysis and visualization of high-throughput single-cell sequencing data. It supports a variety of single-cell technologies, including scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq, and Spatial Transcriptomics (ST).
My workshop on machine learning using python language to implement different algorithms
MXNet Code For Demystifying Neural Style Transfer (IJCAI 2017)
Official code release for "CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity"
TensorFlow implementation of real-time style transfer using feed-forward generation. This builds on the original style-transfer algorithm and allows for common personal computers to transform images.
An image recognition/object detection model that detects handwritten digits and simple math operators. The output of the predicted objects (numbers & math operators) is then evaluated and solved.
[CVPRW 21] "BNN - BN = ? Training Binary Neural Networks without Batch Normalization", Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang
MNIST classification using Multi-Layer Perceptron (MLP) with 2 hidden layers. Some weight-initializers and batch-normalization are implemented.
[WACV 2022] "Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity" by Xinyu Gong, Wuyang Chen, Tianlong Chen and Zhangyang Wang
Win probability predictions for League of Legends matches using neural networks
Classifying audio using Wavelet transform and deep learning
Interesting python codes to tackle simple machine/deep learning tasks
This is a fork of caffe added some useful layers, the original caffe site is https://github.com/BVLC/caffe.
This Repository Contains Solution to the Assignments of the Generative Adversarial Networks (GANs) Specialization from deeplearning.ai on Coursera Taught by Sharon Zhou, Eda Zhou, Eric Zelikman
Tensorflow codes for "Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers"
Unofficial Keras implementation of the paper Attentive Normalization.
Experiments with the ideas presented in https://arxiv.org/abs/2003.00152 by Frankle et al.
We aim to generate realistic images from text descriptions using GAN architecture. The network that we have designed is used for image generation for two datasets: MSCOCO and CUBS.
Classify bird species based on their songs using SIamese Networks and 1D dilated convolutions.
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
Tune-Mode ConvBN Blocks For Efficient Transfer Learning
Short description for quick search
Neural Network implementation in Numpy and Keras. Batch Normalization, Dropout, L2 Regularization and Optimizers
Official implementation for "Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images" https://arxiv.org/abs/2112.08810
A Tensorflow re-implementation of batch renormalization, first introduced by Sergey Ioffe.
Code for "Revisiting Batch Norm Initialization".