Distillation examples. Trying to make Speaker Recognition Faster through different Model Compression techniques
-
Updated
Jul 26, 2020 - Python
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
Code for “Discrimination-aware-Channel-Pruning-for-Deep-Neural-Networks”
[IEEE BigData 2019] Restricted Recurrent Neural Networks
Neural network compression with SVD
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.
Learn linear quantization techniques using the Quanto library and downcasting methods with the Transformers library to compress and optimize generative AI models effectively.
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.
Add a description, image, and links to the model-compression topic page so that developers can more easily learn about it.
To associate your repository with the model-compression topic, visit your repo's landing page and select "manage topics."