Popular GitHub Repositories for Zscore Normalization

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

Microsoft.Recognizers.Text provides recognition and resolution of numbers, units, date/time, etc. in multiple languages (ZH, EN, FR, ES, PT, DE, IT, TR, HI, NL. Partial support for JA, KO, AR, SV). Packages available at: https://www.nuget.org/profiles/Recognizers.Text, https://www.npmjs.com/~recognizers.text

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Persian NLP Toolkit

Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization' [Huang+, ICCV2017]

a pytorch lib with state-of-the-art architectures, pretrained models and real-time updated results

Chinese text normalization for speech processing

SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)

The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

Deep learning library in plain Numpy.

Quick implementation of nGPT, learning entirely on the hypersphere, from NvidiaAI

Tensorflow Implementation of the paper "Spectral Normalization for Generative Adversarial Networks" (ICML 2017 workshop)

pyTorch implementation of Spectral Normalization for Generative Adversarial Networks

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech

Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"

A neural named entity recognition and multi-type normalization tool for biomedical text mining