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jamie-wong.com | ||
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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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dgerrells.com
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| | | | | The challenge, simulate 1,000,000 particles in plain javascript at 60 fps on a phone using only the cpu. Let's go. | |
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ciphrd.com
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| | | | | [latexpage] In this article I will describe a particle simulation that can produce different types of matter based on different input rules. This simulation is based on physical properties and physical rules but the rules were altered in a way that allows for the emergence of intriguing natural-like patterns. The implementation of part 1 and 2 will be done in Processing and the source code will be available at the end. The following illustrations demonstrates a few possible usages for this simulation. | |
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rupertmckay.com
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| | | async/await vs promises. | ||
