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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