Udemy – Quantization for GenAI Models 2024-10

Quantization for GenAI Models course. This course is designed for developers, data scientists, and machine learning enthusiasts who want to build and deploy efficient and optimized AI models. Do you want your models to run faster and with fewer resources, without compromising performance? Are you looking to learn quantization techniques to better deploy your models? During this course, you will learn how to implement quantization techniques and optimize your models for real-world applications. This course provides a complete mix of theory and practical application to increase the performance of machine learning models. By the end of this course, you will have a deep understanding of quantization and be able to optimize and deploy efficient models for edge devices.

What will you learn in this course?

• Understand model optimization techniques: parameter reduction, distillation, and minimization
• Familiarity with data types such as FP32, FP16, BFloat16, and INT8
• Mastering precision reduction from FP32 to BF16 and FP32 to INT8
• Difference between symmetric and asymmetric minimization
• Practical implementation of minimization techniques in Python with real examples
• Using minimization to improve the performance of models and their readiness for deployment
• Gain practical skills to optimize models for edge devices and resource-constrained environments

Who is this course suitable for?

• Developers, data scientists, and machine learning enthusiasts who want to develop efficient and optimized AI models.
• People looking to learn how to minimize models for better deployment.

Quantization for GenAI Models Course Details

• Publisher:  Udemy
• Instructor:  Start-Tech Academy
• Training level: Beginner to advanced
• Training duration: 2 hours and 34 minutes
• Number of lessons: 21

Course syllabus as of 2025/1

Prerequisites for the Quantization for GenAI Models course

Course images

Sample course video

Download link

Download file – 621 MB

File(s) password: www.downloadly.ir

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621 MB