Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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My favorite way to work with multiple filters in pandas.Series β€” not a chain of .loc, but a single mask. 🐼

The chain looks neat, but breaks on real data and easily gives unexpected results:

s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]

The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🀯

It's more reliable to gather everything into one expression:

s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]

One mask, one point of truth. βœ…

It's easier to debug. Fewer surprises when the code grows. πŸš€

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Forwarded from Machine Learning
A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy πŸš€. In many cases, it's enough to replace a single line:

import cupy as cp

The same code can run on CUDA up to 100 times faster ⚑️.

What it can do:
β†’ Compatible with existing NumPy and SciPy code πŸ› οΈ.
β†’ No need to rewrite the program or learn new syntax πŸ“.
β†’ Supports not only CUDA but also AMD ROCm πŸ’».

The project is completely open-source πŸ“‚:
πŸ”— https://github.com/cupy/cupy

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