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.
只需 199 美元(原价 399 美元)即可通过 Coursera Plus 学习更高水平的技能。立即节省

Core generative models and techniques
本课程是 Microsoft Generative AI Engineering 专业证书 的一部分

位教师: Microsoft
包含在 中
您将获得的技能
- MLOps (Machine Learning Operations)
- Prototyping
- Deep Learning
- Image Quality
- Generative Model Architectures
- Forecasting
- Model Evaluation
- Time Series Analysis and Forecasting
- Image Analysis
- PyTorch (Machine Learning Library)
- Tensorflow
- Microsoft Azure
- Generative AI
- Data Pipelines
- Generative Adversarial Networks (GANs)
- Model Deployment
要了解的详细信息

添加到您的领英档案
January 2026
23 项作业
了解顶级公司的员工如何掌握热门技能

积累 Software Development 领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 通过 Microsoft 获得可共享的职业证书

该课程共有4个模块
This foundational module introduces the diverse landscape of core generative models beyond LLMs. You will explore the distinct architectures and principles behind Generative Adversarial Networks (GANs), autoregressive models, and diffusion models. You will also dive into the practical aspects of model creation by comparing essential training frameworks like PyTorch and TensorFlow and learning the fundamental strategies for training these powerful models on Azure.
涵盖的内容
6个视频7篇阅读材料6个作业
This module provides a deep dive into autoregressive models, the engines behind sequential data generation. You will focus on their application in tasks like time-series forecasting and text generation. Starting with the basic principles of next-token prediction, you will use Azure AI Foundry to implement models like TimeGEN-1. You will then advance to sophisticated techniques for controlling model output, ensuring your generated sequences are both coherent and high-quality.
涵盖的内容
5个视频6篇阅读材料5个作业
This module focuses on the cutting-edge technology of diffusion models for creating and editing stunning, high-fidelity images for any purpose. You will learn the fundamental "denoising" process that allows these models to generate photorealistic visuals—from creative compositions to professional graphics—using simple text prompts. You will then move beyond basic generation to master advanced techniques like inpainting, outpainting, and using negative prompts to gain precise control over your visual outputs. This will equip you to produce tailored, high-quality images for a wide array of business and creative applications.
涵盖的内容
5个视频5篇阅读材料6个作业
In this final module, we pivot from code-centric development to a powerful, high-level approach for accelerating model creation. You will master Azure ML Designer, a visual, drag-and-drop environment for rapid prototyping and pipeline development. You will learn to construct, train, evaluate, and prepare sophisticated models for deployment without writing extensive code. This module equips you with essential MLOps skills, enabling you to build and manage the entire machine learning lifecycle efficiently.
涵盖的内容
6个视频6篇阅读材料6个作业
获得职业证书
将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。
从 Software Development 浏览更多内容
状态:免费试用Simplilearn
状态:免费试用Pearson
状态:免费试用
人们为什么选择 Coursera 来帮助自己实现职业发展




常见问题
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
更多问题
提供助学金,




