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Learning Generative Adversarial Networks

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Learning Generative Adversarial Networks
Learning Generative Adversarial Networks
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours | Lec: | 460 MB
Genre: eLearning | Language: English

Generative models are gaining a lot of popularity among data scientists, mainly because they facilitate the building of AI systems that consume raw data from a source and automatically build an understanding of it.

Unlike supervised learning methods, generative models do not require labeling data, which makes for an interesting system to use. This video will help you build and analyze deep learning models and apply them to real-world problems. It will help readers develop intelligent and creative application from a wide variety of datasets, mainly focusing on visuals or images.

The video begins with the basics of generative models, as you get to know the theory behind Generative Adversarial Networks and its building blocks. In this video, you'll see how to overcome the problem of text-to-image synthesis with GANs, using libraries such as Tensorflow, Keras, and PyTorch.

Learning Generative Adversarial Networks

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  1. 学习生成式对抗网络 生成式模型在数据科学家中正获得非常高的流行度,主要是因为它们能实现从一个源和对其理解儿自动开发而成的使用原始数据的AI系统的开发。 与监督式学习方法不同,生成式模型不需要对数据标记,这使其在使用时是一个有趣的系统。本教程将会帮助你开发和分析深度学习模型,并应用他们与真正的问题。本教程还会帮助读者,从广泛的数据集中开发智能的并且具有创意的应用,这主要集中于视觉和图像。 本教程将从生成式模型的基础知识开始,随着学习你会了解生成式对抗网络背后的理论机器构建块。在本教程中,你将看到如何通过GANs,使用如Tensorflow、Keras和PyTorch这样的类库解决文字—图像同步的问题。
    wilde(特殊组-翻译)5个月前 (02-26)