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DeepLearning.AI

Marc Sun

Younes Belkada

In this course, you will implement and customize linear quantization from scratch, studying the tradeoff between space and performance. You will build a general-purpose quantizer in PyTorch to quantize any open source model, compressing model weights from 32 bits to 8 bits and even 2 bits. This course provides the foundation to explore more advanced quantization methods.

Quantization in Depth

Overview of quantization and its importance in model compression.

Introduction to Quantization

Detailed exploration of linear quantization, including symmetric and asymmetric modes.

Linear Quantization Methods

Understanding different granularities like per-tensor, per-channel, and per-group quantization.

Granularities in Quantization

Step-by-step guide to building a general-purpose quantizer in PyTorch.

Building a Quantizer in PyTorch

Techniques to measure and balance the tradeoff between performance and space.

Quantization Error Measurement

Implementing weights packing to pack four 2-bit weights into a single 8-bit integer.

Advanced Quantization Techniques

Real-world applications and case studies of quantization in machine learning models.

Case Studies and Applications

Exploring advanced quantization methods and future research directions.

Future Directions in Quantization

In Quantization in Depth, you will build model quantization methods to shrink model weights to ¼ their original size while maintaining performance. This course will help you make your models more accessible and faster at inference time.

Professionals looking to deepen their understanding of linear quantization methods.

Machine Learning Engineers

Individuals interested in making their models more accessible and faster at inference time.

Data Scientists

Learners who have completed the Quantization Fundamentals course and want to explore advanced quantization techniques.

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Quantization in Depth

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