2022/01-25
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|title= Scientific Computing in Python: A NumPy Crash-Course | |title= Scientific Computing in Python: A NumPy Crash-Course | ||
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|presenters=Mark Chilenski | |presenters=Mark Chilenski | ||
|location=1-190 | |location=1-190 | ||
|abstract=NumPy is the standard tool for manipulating arrays of numeric data in Python, and has heavily influenced the design and implementation of many other libraries for scientific computing and machine learning. This talk will be a self-contained "crash course," intended to get someone with little or no NumPy experience to the point that they can confidently start manipulating arrays. The talk will emphasize principles such as broadcasting and vectorization which will also help intermediate NumPy users to write cleaner, more efficient code. | |abstract=NumPy is the standard tool for manipulating arrays of numeric data in Python, and has heavily influenced the design and implementation of many other libraries for scientific computing and machine learning. This talk will be a self-contained "crash course," intended to get someone with little or no NumPy experience to the point that they can confidently start manipulating arrays. The talk will emphasize principles such as broadcasting and vectorization which will also help intermediate NumPy users to write cleaner, more efficient code. | ||
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Latest revision as of 05:01, 9 September 2022
[edit] Scientific Computing in Python: A NumPy Crash-Course
| Date: January 25, 2022, at 6:00 PM |
| Presenters: Mark Chilenski |
| Location: 1-190 |
| Abstract: NumPy is the standard tool for manipulating arrays of numeric data in Python, and has heavily influenced the design and implementation of many other libraries for scientific computing and machine learning. This talk will be a self-contained "crash course," intended to get someone with little or no NumPy experience to the point that they can confidently start manipulating arrays. The talk will emphasize principles such as broadcasting and vectorization which will also help intermediate NumPy users to write cleaner, more efficient code. |
