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

Cut models not trees 🌳

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)

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

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

Model Compression CLI Tool for Keras.

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

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.

A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation for Efficient Hardware Acceleration on Edge Devices

awesome machine learning / deep learning papers

An Integrated Distributed Deep Learning (IDDL) framework.