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Description
If you are a developer, data scientist, or machine learning enthusiast who wants to optimize and deploy efficient AI models, this course is for you. Do you want to make your models faster and more resource-efficient while maintaining performance? Are you looking to learn how to apply quantization techniques for better model deployment? This course will teach you how to implement practical quantization techniques, making your models lean and deployable on edge devices.
In this course, you will:
Learn the core concepts of Quantization, Pruning, and Distillation.
Understand different data types like FP32, FP16, BFloat16, and INT8.
Explore how to convert FP32 to BF16 and INT8 for efficient model compression.
Implement symmetric and asymmetric quantization in Python with real-world applications.
Understand how to downcast model parameters from FP32 to INT8 for deployment.
Gain hands-on experience with Python-based quantization, making your models suitable for mobile and IoT devices.
Why learn quantization? Quantization allows you to reduce the size and computational load of models, making them suitable for resource-constrained devices like smartphones, IoT devices, and embedded systems. By mastering quantization, you can ensure your models are faster, more energy-efficient, and easier to deploy while maintaining accuracy.
Throughout the course, you’ll learn to implement quantization techniques and optimize your models for real-world applications. This course provides the perfect balance of theory and practical application for making machine learning models more efficient.
By the end of the course, you’ll have a deep understanding of quantization, and the ability to optimize and deploy efficient models on edge devices.
Ready to optimize your AI models for efficiency and performance? Enroll now and start your journey!
