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www.danieldjohnson.com | ||
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blog.evjang.com
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| | | | | This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and ... | |
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www.depthfirstlearning.com
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| | | | | [AI summary] The provided text is a comprehensive set of notes and exercises covering various topics in Generative Adversarial Networks (GANs) and their improvements, including standard GANs, Wasserstein GANs (WGANs), and WGAN with Gradient Penalty (WGAN-GP). The content includes theoretical explanations, practical implementation tasks, and discussion of challenges and solutions in training GANs. Key topics include the mathematical foundations of GANs, the limitations of standard GANs (such as mode collapse and sensitivity to hyperparameters), the introduction of WGANs to address these issues through the Wasserstein distance, and further improvements with WGAN-GP to mitigate problems like weight clipping instability. The text also includes exercises for calc... | |
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www.unite.ai
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| | | | | Some machine learning models belong to either the generative or discriminative model categories. Yet what is the difference between these two categories of models? What does it mean for a model to be discriminative or generative? The short answer is that generative models are those that include the distribution of the data set, returning a [] | |
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yasoob.me
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| | | Hey guys! I recently wrote a review paper regarding the use of Machine Learning in Remote Sensing. I thought that some of you might find it interesting and insightful. It is not strictly a Python focused research paper but is interesting nonetheless. Introduction to Machine Learning and its Usage in Remote Sensing 1. Introduction Machines have allowed us to do complex computations in short amounts of time. This has given rise to an entirely different area of research which was not being explored: teachin... | ||
