Python-bloggers

What is Transfer Learning? Transfer Learning (TL) is one of the most powerful methods for building high-performance deep learning models in computer vision. TL is based on the knowledge-reusability concept – one can use knowledge from one area and apply it to another. By leveraging previous experience, you don’t need ...

Sometimes, while working with the Jupyter notebook you need to create a file, like a .py file. Today we will ... Read moreHack: How to Create files from Jupyter

Don’t we all miss 2019 (blame Covid for the long delay in this post). The days of going to work and seeing your work colleagues face to face - and for some of you, attending one of our on-site training courses! 2019 was a great year for us. Not only have ...

Words Sentiment Score We have explained how to get a sentiment score for words in Python. Instead of building our ... Read moreHow to Run Sentiment Analysis in Python using VADER

We will show how you can create bins in Pandas efficiently. Let’s assume that we have a numeric variable and ... Read moreHow to create Bins in Python using Pandas

We recently read two blog posts from Robot Wealth and FOSS Trading on calculating rolling pairwise correlations for the constituents of an S&P 500 sector index. Both posts were very interesting and offered informative ways to solve the problem using different packages in R: tidyverse or xts. We’ll use ...

The third in the series of Python Pandas tutorials. In this session we look at setting values, performing operations on data frames and histogramming.

In the previous post we worked with a custom data set we had created for the purposes of demonstration. This time we are going to work with the gapminder dataset. Viewing the gapminder dataset To initialise the gapminder dataset in my project I will use the below import statements to ...

tl;dr Convolutional Neural Networks (CNN) are used for the majority of applications in computer vision. You can find them almost everywhere. They are used for image and video classification and regression, object detection, image segmentation, and even playing Atari games. Understanding the convolution layer is critical in building successful ...