A Practical Introduction to Model Compression

Date:

Thursday, November 16, 2023

Time:

1:30 pm

Room:

Saphir 2+3

Summary:

Machine learning models have become increasingly complex and resource-intensive, posing significant challenges. Model compression techniques addresses this issue by reducing the size and computational requirements of these models without significant loss in performance. This talk gives a comprehensive overview and code walk through of machine learning model compression techniques. (Model Pruning, Knowledge Distillation, Quantization, and low-rank approximation etc)

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