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victoria.dev | ||
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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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skerritt.blog
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| | | | | Greedy algorithms aim to make the optimal choice at that given moment. Each step it chooses the optimal choice, without knowing the future. It attempts to find the globally optimal way to solve the entire problem using this method. Why Are Greedy Algorithms Called Greedy? We call algorithms greedy when | |
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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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brandont.dev
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