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theorydish.blog | ||
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newvick.com
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| | | | | I've been working my way through Andrej Karpathy's 'spelled-out intro to backpropagation', and this post is my recap of how backpropagation works. | |
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jingnanshi.com
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| | | | | Tutorial on automatic differentiation | |
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thenumb.at
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| | | | | [AI summary] This text provides a comprehensive overview of differentiable programming, focusing on its application in machine learning and image processing. It explains the fundamentals of automatic differentiation, including forward and backward passes, and demonstrates how to implement these concepts in a custom framework. The text also discusses higher-order differentiation and its implementation in frameworks like JAX and PyTorch. A practical example is given using differentiable programming to de-blur an image, showcasing how optimization techniques like gradient descent can be applied to solve real-world problems. The text emphasizes the importance of differentiable programming in enabling efficient and flexible computation for various domains, includ... | |
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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 [] | ||
