Python-bloggers

HuggingFace have been working on a model that can be used for small datasets. The aim is to leverage the pretrained transformer and use contrastive learning to augment and extend the dataset, by using similar labels that share a same dimensional space. In this tutorial I will talk you through ...

Between Power Query and Power Pivot alone, “modern” Excel boasts so many features that it’s impossible to learn them all. Add to that Office Scripts, Power BI, LAMBDA() and more and it’s easy to get overwhelmed. One “Excel-adjacent” tool that can be particularly intimidating for prospective learners is ...

Scikit-learn is a powerful Python library widely used for performing complex AI and machine learning (ML) tasks. It is an open-source library that provides numerous robust algorithms, which include regression, classification, dimensionality reduction, and clustering techniques. In this tutorial, we will explore some powerful functions of scikit-learn using scikit-learn toy ...

Applied statistics is dominated by the ubiquitous mean. For a change, this post is dedicated to quantiles. I will give my best to provide a good mix of theory and practical examples. While the mean describes only the central tendency of a distribution or random sample, quantiles are able to ...

Introduction ‘All that is solid melts into the air’ Karl Marx and Friedrich Engels As a historian and social scientist I often find it challenging to customize my Python plots and make them clear and compelling. What is a Python class, object ...

As data scientists we often need to communicate conclusions drawn from data. Additionally, as more data is collected, our reports invariably need updating. This is where automated reporting tools such as Quarto come in! In this blog post we will look at how Quarto allows us to weave together text ...

Machine learning is a fast-growing technology that allows computers to learn from the past and predict the future. It uses numerous algorithms for building mathematical models and predicting future trends. Machine learning (ML) has widespread applications in the industry, including speech recognition, image recognition, churn prediction, email filtering, chatbot development, ...

Artificial intelligence (AI) has widespread applications in the real world, such as healthcare, robotics, autonomous vehicles, retail, eCommerce, etc. Together, AI, machine learning (ML), and deep learning (DL) solve various academic and industrial problems.  Due to AI’s accurate predictive capabilities, the use of AI has skyrocketed over the last ...

At Jumping Rivers we’re all about getting involved in the R community! As such, we host multiple events throughout the year. Read on for information about what we have planned so far for 202...

In January 2023, I traveled to Santa Barbara, sunny Californ-i-a to record two data analytics courses with Madecraft. There will be plenty of opportunity to discuss the contents of these courses, but for now I’d like to reflect on the process of recording them in the studio. Making a video ...