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學生對 deeplearning.ai 提供的 Build Better Generative Adversarial Networks (GANs) 的評價和反饋

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544 個評分

課程概述

In this course, you will: - Assess the challenges of evaluating GANs and compare different generative models - Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs - Identify sources of bias and the ways to detect it in GANs - Learn and implement the techniques associated with the state-of-the-art StyleGANs The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research....

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GJ

2020年9月30日

Very good course! Helpful to understand evaluation metrics and details of Style GAN. It was also super cool to have the bias section that is not as well known as the others. Loved it!

AB

2021年3月24日

Great material...but the stylegan code implementation requires more video material. Instead adding one more week for ProGan part before stylegan would be helpful for the learners.

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76 - Build Better Generative Adversarial Networks (GANs) 的 81 個評論(共 81 個)

創建者 Karan S

2022年5月16日

創建者 Michael K

2020年11月6日

創建者 Shounak D

2022年7月31日

創建者 Daniil K

2021年8月28日

創建者 Злобин Я Н

2021年8月8日

創建者 Bedrich P

2021年8月21日