We cover basic mistakes that can lead to unnecessary copying of data and memory allocation in NumPy. We further cover NumPy internals, strides, reshaping, and transpose in detail.
In Part 1 of our series on writing efficient code with NumPy we cover why loops are slow in Python, and how to replace them with vectorized code. We also dig deep into how broadcasting works, along with a few practical examples.
In this article we'll start with an introduction to gradient boosting for regression problems, what makes it so advantageous, and its different parameters. Then we'll implement the GBR model in Python, use it for prediction, and evaluate it.
In this tutorial we use Cython to reduce the execution time of the genetic algorithm implemented in Python. We've brought down our computational time from 1.46 seconds to a mere 0.08 seconds, so that 1 million generations run in less than 10 seconds with Cython, compared to 180 seconds in Python.
In this tutorial we'll see how you can take your work and give it an audience by deploying your projects on the web
Here we see how to speed up NumPy array processing using Cython. By explicitly declaring the "ndarray" data type, your array processing can be 1250x faster.
In this tutorial we'll train CycleGAN with Keras to generate images which age a subject's face, either forwards or backwards.
