Maximizing Python Speed with Numpy: Complexity (Part 2)
In the last video, our benchmark for the algorithm was 6 minutes and 33.6 seconds. But we can do much better.
How do we know that? In this video, we will analyze the complexity of algorithm and
tell you why we think we can get to 4 seconds
show you how to reduce to 6 seconds with numpy
2:11 A simplified version of code
3:20 In-depth analysis
7:50 Why do we know we can do better?
9:37 Speed estimate in human time
11:43 The reason why we can do better
14:11 Use numpy to modify code to speed up
Previous video in the series:
Maximizing Python Speed with Numpy Vectorization (Part 1)
• Maximizing Python Speed with Numpy Vectori...
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