Fashion-MNIST data from PyTorch

Visualizing Fashion MNIST data images

In this short tutorial we will learn how to get access to Fashion-MNIST dataset. Fashion-MNIST is one of the image datasets useful for learning and training Deep Learning algorithms in action. PyTorch’s torchvision module makes it easy access Fashion-MNIST dataset. Fashion-MNIST dataset is from Zalando’s article images and it contains a training set of 60,000… Continue reading Fashion-MNIST data from PyTorch

Pandas case_when() with multiple examples

The newest Pandas release Pandas 2.2.0 has one of the most useful functions case_when() available on a Pandas Series object. Often you might want to create a new variable from an existing variable using multiple conditions. For a simple binary condition we can use Pandas’ where() function. With the new case_when() function we can apply… Continue reading Pandas case_when() with multiple examples

An Introduction to Statistical Learning: with Applications in Python Is Here

An Introduction to Statistical Learning: with Applications in Python

An Introduction to Statistical Learning in R ISLR, one of the best books to learn statistical learning, has a cousin now, Statistical Learning in Python. An Introduction to Statistical Learning in Python by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor has been out recently. And just like the ISL in R,… Continue reading An Introduction to Statistical Learning: with Applications in Python Is Here

8 Plot types with Matplotlib in Python

Heatmap with Matplotlib imshow() function

Matplotlib, the most comprehensive visualisation library in Python for creating all kinds of plots of data visualization. However, it can also be a bit frustrating and daunting given so much you can do with Matplotlib. In this post, we will learn how to use 8 commonly used plot types, like scatter plot, histogram, with real… Continue reading 8 Plot types with Matplotlib in Python

How to do QR Decomposition in Python with Numpy

QR decomposition, also known as QR factorization, is a method for decomposing a matrix into the product of an orthogonal matrix and an upper-triangular matrix. It is a useful tool for solving systems of linear equations, computing the inverse of a matrix, and computing the singular value decomposition (SVD) of a matrix. In this blog… Continue reading How to do QR Decomposition in Python with Numpy