Python Lists Are Sometimes Much Faster Than NumPy

Python Lists Are Sometimes Much Faster Than NumPy

Python Lists Are Sometimes Much Faster Than NumPy. You will definitely be amazed after reading this article

Be careful what to use

I have been recently working on a digital image processing project. Hyperparameter tuning took quite some time before I got the desired accuracy. All because of the overfitting parasite and my useless low-end hardware.

For each execution, my machine took approximately 15–20 min. 20 min to process 20 000 entries. I imagine if I had been working on a 1 million record dataset, I would have had to wait for the earth to do a complete rotation before the end of the training.

I was satisfied with the accuracy of the model. Yet, I wanted to try many other Convolutional Neural Network (CNN) architectures before sending in my code. Therefore, I decided to look for optimization rooms in my code.

Because I was using the pre-built machine learning algorithms residing in PyPi — Scikit-Learn and Tensorflow — very few subroutines were left to optimize. One option was to boost my code in terms of data structure. I was storing data in lists, and since NumPy is super fast, I thought using it might be a viable option.

Guess what happened after converting my list code to NumPy array code?

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