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14 de mai. de 2024 · 322 subscribers. Subscribed. 0. No views 6 minutes ago. This video offers a detailed explanation of the main concepts covered in Chapter 1 of the 'Deep Learning' book by Ian Goodfellow, Yoshua...
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- Sardor Abdirayimov
Há 6 dias · Ian Goodfellow's work on GANs has earned him widespread recognition and acclaim within the AI community. GANs are now a fundamental topic in AI research and education, with their principles...
Há 18 horas · Deep Learning (Ian Goodfellow, Yoshua Bengio, Aaron Courville) Um livro fundamental que cobre teorias e aplicações de deep learning. Neural Networks and Deep Learning (Michael Nielsen) Um livro online que explica redes neurais de forma intuitiva e acessível. The Hundred-Page Machine Learning Book (Andriy Burkov)
Há 1 dia · One of the key developments in the history of generative AI was the introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow and co in 2014. GANs consist of two neural networks—a generator and a discriminator—that compete against each other, creating highly realistic images, videos, and other content.
6 de mai. de 2024 · Building Generative Adversarial Networks. Learn to understand and implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs.
Há 4 dias · Alexey Kurakin, Ian Goodfellow, and Samy Bengio. Adversarial examples in the physical world, 2017. Google Scholar; Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. Google Scholar Cross Ref; Yan Li, Ethan X.Fang, Huan Xu, and ...
22 de mai. de 2024 · One of the most significant breakthroughs came with the introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow and colleagues in 2014. GANs consist of two neural networks, the generator and the discriminator, locked in a competitive learning process, resulting in astonishingly realistic outputs.
