Distillation examples. Trying to make Speaker Recognition Faster through different Model Compression techniques
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Updated
Jul 26, 2020 - Python
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”
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.
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.
awesome machine learning / deep learning papers
An Integrated Distributed Deep Learning (IDDL) framework.
Dive into advanced quantization techniques. Learn to implement and customize linear quantization functions, measure quantization error, and compress model weights using PyTorch for efficient and accessible AI models.
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