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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.

Educational deep learning library in plain Numpy.

Adaptive Affinity Fields for Semantic Segmentation

Deep Learning Specialization courses by Andrew Ng, deeplearning.ai

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

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)).

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

MXNet Code For Demystifying Neural Style Transfer (IJCAI 2017)

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.

Using slim to perform batch normalization

[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

Official code release for "CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity"