Core generative models and techniques

Coursera MOOC / Non-credit USD 49
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Core generative models and techniques

About this course

Explore the diverse and powerful world of core generative AI. This course provides a comprehensive survey of the fundamental models that power modern AI, including Generative Adversarial Networks (GANs), autoregressive models, and diffusion models. You will build a strong foundation, understanding the unique architectures and training strategies for each, and compare essential frameworks like PyTorch and TensorFlow. The course then moves into hands-on implementation. You will learn to generate sequential data, such as time-series forecasts, using advanced autoregressive models in Azure AI Foundry. Next, you will master the art of high-fidelity image generation, using diffusion models to create and edit stunning visuals with techniques like inpainting and outpainting. Finally, you will learn to accelerate your development workflow by using Azure ML Designer, a visual, low-code environment for rapid prototyping. You will practice designing, building, evaluating, and preparing sophisticated model pipelines for real-world deployment. This course equips you not just with knowledge of different models, but with the practical skills to build and prototype them effectively on Azure.

What you'll learn

  • understanding of Generative Adversarial Networks (GANs)
  • knowledge of autoregressive models and diffusion models
  • ability to generate sequential data using advanced models
  • skills in high-fidelity image generation and manipulation
  • proficiency in using Azure ML Designer for model pipelines

Course objectives

  • provide a comprehensive survey of generative models
  • enable hands-on implementation of core generative techniques
  • develop practical skills for building and prototyping models on Azure

Skills you'll gain

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