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www.jeremykun.com | ||
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degoes.net
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| | | | | Functional programming has a bit of jargon, but that doesn't have to stop you from understanding core concepts | |
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bayesianneuron.com
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| | | | | [AI summary] The user has shared a detailed exploration of optimizing the 0/1 Knapsack problem using dynamic programming with Python and NumPy. They discuss various optimization techniques, including reducing memory usage with a 2-row approach, vectorization using NumPy's `np.where` for faster computation, and the performance improvements achieved. The final implementation shows significant speedups, especially for large-scale problems, and the user highlights the importance of vectorization and efficient memory management in computational tasks. | |
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lukesingham.com
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| | | | | Grokking Algorithms is a beautifully formatted book that explains complex material simply using pictures, analogies and high level practical explanations. This post is a review and summary of the Grokking Algorithms book. | |
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www.jeremykun.com
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| | | Table of Contents In this article we'll implement a global optimization pass, and show how to use the dataflow analysis framework to verify the results of our optimization. The code for this article is in this pull request, and as usual the commits are organized to be read in order. The noisy arithmetic problem This demonstration is based on a simplified model of computation relevant to the HEIR project. You don't need to be familiar with that project to follow this article, but if you're wondering why s... | ||
