Image Processing is one of the most crucial tasks when dealing with images. It typically means manipulating images like converting the original image to grayscale, computing distance maps, performing segmentation, etc. One such cumbersome task is finding the distance map or distance transformation between two different regions of an image which will be used to …
Python Equivalent to Matlab’s Bwdist: A Comprehensive Guide Read More »
Coupled differential equations and why they are important to our understanding will be learned in this article How to solve coupled differential equations using NumPy is the main objective of this article. A robust Python package used for calculations is called NumPy. To learn more about NumPy read the linked article. What are Differential Equations? …
Python NumPy: Solving Coupled Differential Equations Read More »
The approximate solution of complicated mathematical functions depends critically on numerical integration. Providing remarkably accurate results by carefully choosing nodes and weights, the Gauss-Legendre Quadrature method is a robust numerical integration method. Precise answers to a variety of integration problems will be revealed, and we will explore how we can put this approach into practice …
The Python downsampling approach will be explored, and an interesting visit into the world of image processing will be taken in this article. A key ability for faster processing and effective memory management is learning the concepts of image scaling and maintaining the original image. We will explore the power of downsampling together. Image Processing: …
Downsampling Arrays Image Processing using Python. Read More »
Numpy is a go-to tool used for statistics, and auto-covariance is a statistical concept. In this article, we shall study how we can calculate auto-covariance using NumPy. Definition of Auto-Covariance Auto-covariance is a concept used in statistics that is used to calculate covariance in a time series and its lagged version at various points in …
Bias and variance represent distinct concepts in the fields of Machine Learning and Deep Learning. The primary objective when working with any machine learning model is to achieve accuracy. By striking a balance between these two sources of error(bias and variance), commonly known as the Bias-Variance tradeoff, we can enhance prediction accuracy. This article explores …
When considering complex computations in scientific computing, data analysis, and manipulation, matrices play a very important role in storing data and performing certain calculations. The properties of a matrix play a significant role in this process. One such property of a matrix is its Transpose. To define in simple terms, the transpose of a matrix …
In the domain of machine learning and pattern recognition, a square matrix called the Gaussian kernel matrix, also known as a radial basis function (RBF) kernel matrix, holds great significance. Its purpose is to represent the degree of similarity or distance between pairs of data points within a dataset. This valuable tool finds wide application …
Cross entropy is a differentiative measure between two different types of probability. Cross entropy is a term that helps us find out the difference or the similar relation between two probabilities. There are two different types of distributions in any model i.e. The predicted probability distribution and the actual distribution, or true distribution. Cross entropy …
Einsum is an almighty function from the numpy library which is the most efficient manipulator of n-dimensional arrays. It can perform umpteen functions such as adding, multiplying or rearranging the input arrays in a jiffy, resulting in multiple x’s faster computation. Some implications of einsum might be interesting enough that they have no relation whatsoever …
Mastering NumPy’s Powerful einsum_path( ) Function Read More »