Distillation examples. Trying to make Speaker Recognition Faster through different Model Compression techniques

Industry 4.0 collaborations with Control2K, for using AI on IOT devices to analyse factory machinery

deep learning model compression with pruning

Versioning System for Online Learning systems (VSOL)

Cut models not trees 🌳

Code for “Discrimination-aware-Channel-Pruning-for-Deep-Neural-Networks”

Model Compression CLI Tool for Keras.

Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization

analysing Model Pruning and Unit Pruning on a large dense MNIST network

This repository includes a general informations and examples about how to make a machine learning model just a few lines of code in Python using PyCaret package.

Transformers Compression Practice

[IEEE BigData 2019] Restricted Recurrent Neural Networks

Neural network compression with SVD

Learn linear quantization techniques using the Quanto library and downcasting methods with the Transformers library to compress and optimize generative AI models effectively.

Code for paper - Experience Loss in PyTorch.

Neural Network Compression

SVC and KNN methods were used to predict whether mushrooms are poisonous or edible according to their properties. Random forest and chi-square variable selection methods were applied and the 10-fold cross validation method was used and f1 scores were calculated by re-estimating. Finally, the models were compared.

ai-zipper offers numerous AI model compression methods, also it is easy to embed into your own source code

Library for compression of Deep Neural Networks.