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blog.otoro.net | ||
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kvfrans.com
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| | | | | In my previous post about generative adversarial networks, I went over a simple method to training a network that could generate realistic-looking images. However, there were a couple of downsides to using a plain GAN. First, the images are generated off some arbitrary noise. If you wanted to generate a | |
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michael-lewis.com
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| | | | | This is a short summary of some of the terminology used in machine learning, with an emphasis on neural networks. I've put it together primarily to help my own understanding, phrasing it largely in non-mathematical terms. As such it may be of use to others who come from more of a programming than a mathematical background. | |
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www.v7labs.com
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| | | | | What are Generative Adversarial Networks and how do they work? Learn about GANs architecture and model training, and explore the most popular generative models variants and their limitations. | |
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iclr-blogposts.github.io
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| | | 'The Primacy Bias in Deep Reinforcement Learning' demonstrates how the first experiences of a deep learning model can cause catastrophic memorization and how this can be prevented. In this post we describe primacy bias, summarize the authors' key findings, and present a simple environment to experiment with primacy bias. | ||
