Computer Vision How To Speed Up Object Detection Using NumPy Reshape and Transpose This is Part 4 of our ongoing series on NumPy optimization. In Parts 1 and 2 we covered the concepts of vectorization and broadcasting, and how they can be applied
NumPy 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.
NumPy 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.
NumPy 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.
