Optimization
By
Anuj Sable
Nuts and Bolts of NumPy Optimization Part 3: Understanding NumPy Internals, Strides, Reshape and Transpose
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.
Nuts and Bolts of NumPy Optimization Part 2: Speed Up K-Means Clustering by 70x
In this part we'll see how to speed up an implementation of the k-means clustering algorithm by 70x using NumPy. We cover how to use cProfile to find bottlenecks in the code, and how to address them using vectorization.
Nuts and Bolts of NumPy Optimization Part 1: Understanding Vectorization and Broadcasting
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.
Neural Architecture Search Part 1: An Overview
With countless options to design neural networks, an effective architecture search algorithm would be game-changing. Here we look at the state of the art.
By
Anuj Sable
NumPy Array Processing With Cython: 1250x Faster
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.
Boosting Python Scripts With Cython
Python might be one of today's most popular languages, but it's definitely not the most efficient. See how Cython can easily boost your Python scripts.