When I first started visualizing complex datasets, I often relied on box plots to see the distribution of my data. However, I quickly realized that box plots can hide important details, like whether a distribution has multiple peaks. That is when I discovered violin plots in Matplotlib, which combine a box plot with a kernel … Read more >>
When I first started building custom data visualizations in Python, I often found myself needing to highlight specific regions on a chart. Whether it’s marking recession periods in economic data or outlining specific clusters in a scatter plot, knowing how to draw shapes is a fundamental skill. In this tutorial, I will show you exactly … Read more >>
Plotting data with vastly different scales on the same chart can be a real headache. I’ve often found myself staring at a flat line on a linear scale, simply because one dataset overshadowed the other. In this tutorial, I will show you how to implement a secondary Y-axis and apply a logarithmic scale. This is … Read more >>
When I build dashboards for financial analysis, I often need to compare two different metrics that don’t share the same scale. For instance, you might want to visualize the S&P 500 index price alongside the daily trading volume. If you plot them on the same Y-axis, the volume (in millions) will completely flatten the index … Read more >>
In my years of developing data-driven applications in Python, I’ve found that Matplotlib’s default scaling is usually great, but it isn’t perfect. There are many times when the default view starts the axis at a value that hides the trend you are trying to highlight. I often find myself needing to force a plot to … Read more >>
One of the most common challenges I face when building complex data visualizations is managing scales. Sometimes, a single Y-axis just doesn’t tell the whole story. In my experience, adding a secondary axis is the best way to compare two different data scales on a single chart. However, getting the range right can be tricky. … Read more >>
I have spent years visualizing complex datasets using Matplotlib, and one of the most common hurdles I see developers face is handling the axis range in imshow. By default, Matplotlib treats your array indices as coordinates, which rarely matches the real-world scale of your data. In this tutorial, I will show you exactly how to … Read more >>
Over my four years as a Keras developer, I have spent countless hours debugging backpropagation gradients. It is often frustrating when gradients vanish or explode during deep network training. Recently, I started experimenting with Geoffrey Hinton’s Forward-Forward (FF) algorithm as a powerful alternative. It replaces the traditional backward pass with two forward passes, one with … Read more >>
If you have been building vision models with Keras, you likely know that Self-Attention is the “gold standard” for capturing long-range dependencies. However, after four years of scaling these models, I have found that Self-Attention often struggles with high-resolution images because its complexity grows quadratically with the number of pixels. Recently, I started using Focal … Read more >>
I have spent the last four years building deep learning pipelines, and one thing is clear: Vision Transformers (ViT) are incredibly powerful but often too bulky for real-time applications. In this tutorial, I will show you how to use Keras to distill the knowledge from a large ViT model into a much smaller, faster neural … Read more >>
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