Machine Learning

Any periodic signal can be decomposed into a set of simple oscillating functions (also known as harmonics) via the application of Fourier series expansion. In this post, we demonstrate how harmonics can be visualized using circles spinning at multiples of a fundamental frequency.

> We showcase each harmonic individually and then demonstrate how they add up to form the resulting function. This visualization technique helps to understand the Fourier Transform, a powerful mathematical tool used in many applications, including machine learning.

> In computer vision and image processing, the Fourier Transform is valuable for feature extraction and dimensionality reduction. For instance, it can decompose an image into its component frequencies, allowing for a more efficient representation as input for a machine learning algorithm.

> This approach enables the algorithm to focus on the essential features of the image rather than processing the entire image at once, which can be computationally expensive. The Fourier Transform is a useful tool for reducing the complexity of an image and extracting valuable information.

> The Fourier Transform is also used in Convolutional Neural Networks (CNNs) for constructing filters applied to image inputs. These filters use a combination of Fourier Transform and neural network techniques to learn features from the images and perform various computer vision tasks.

>> Follow me for daily doses of informative content! I share valuable insights, tips, and techniques related to machine learning, data science, and other exciting fields. Let's explore the fascinating world of mathematics and computer science together!

[Read more:?https://bit.ly/3y8XNca]

#computervision?#computerscience?#complexity

Loaded:?100.00%