Quantization in Deep Learning (LLMs)

This video is about quantization in deep learning in the deep learning tutorial series.

Quantization is getting more and more popular and essential to deal with the ever-growing deep learning models. But how does quantization work? What are the different types of quantization algorithms? What are the different models of quantization? I have tried to answer these questions in this video.

Topics covered include Types of Quantization - Uniform and Non-Uniform quantization, and further divisions of Uniform quantization such as symmetric and asymmetric quantization, dequantization, choosing the scale factor and zero point parameters for both symmetric and asymmetric quantization. Lastly, Post-training quantization or PQT and Quantization Aware Training or QAT are also covered.

A practical guide to neural network quantization both in PyTorch and TensorFlow is to follow.

As always, hope it's useful!

RELATED LINKS
Efficient Deep Learning Computing: https://efficientml.ai
Lei Mao's Blog: https://leimao.github.io/article/Neur...
Survey paper on Quantization: https://arxiv.org/abs/2103.13630

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