Python-bloggers

Excel has long been a go-to tool for data analysts to crunch numbers and perform data analysis. Traditionally, its ability to handle text data, particularly for advanced analysis tasks like sentiment analysis, was seen as limited. Analysts seeking meaningful insights from text often resorted to more specialized tools or cumbersome ...

When Copilot for Excel was first introduced, it primarily served as a tool to automate basic Excel tasks like sorting, filtering, conditional formatting, and creating formulas. However, as the AI has evolved and more resources have been invested in the product, it now supports more advanced functionalities, including sophisticated data ...

It has been almost 2 months since a few of our developers tried using the Robot framework to make the automation process smoother and cleaner. They prefer choosing this framework because it’s easy to use and offers an extensive array of libraries.   The capabilities it provided to our developers were ...

Advanced Analysis with Python in Copilot for Excel can help with many data analysis tasks that would typically be challenging to execute in Excel. From building predictive models using techniques like random forests to uncovering patterns and relationships through clustering and principal component analysis, and even analyzing text data for ...

Gradient checkpointing has emerged as a pivotal technique in deep learning, especially for managing memory constraints while maintaining high model performance. In the rapidly evolving field of AI, out-of-memory (OOM) errors have long been a bottleneck for many projects. Gradient checkpointing, particularly in PyTorch, offers an effective solution by optimizing ...

Interacting with Techtonique web app through Excel, using Visual Basic for Applications (VBA).

The video below provides several different examples and ways that data can be imported into Python.

Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization

In the realm of statistical analysis, critical values and p-values serve as essential tools for hypothesis testing and decision making. These concepts, rooted in the work of statisticians like Ronald Fisher and the Neyman-Pearson approach, play a crucial role in determining statistical significance. Understanding the distinction between critical values and ...