I have frequently dealt with time-series data. Whether it is tracking stock prices on the NYSE or analyzing California’s energy consumption, managing the x-axis scale is a constant task. When you plot dates in Python using Matplotlib, the library often does a great job of guessing the range. However, there are many times when you … Read more >>
I have spent years building data visualization dashboards using Python for various tech firms. One thing I have learned is that a plain pie chart rarely tells the whole story to your stakeholders. In my experience, a chart without clear annotations is just a collection of colored slices that confuses the audience. I found that … Read more >>
I have spent years building data visualization dashboards for various American tech firms. One thing I have learned is that a well-crafted pie chart is still one of the most effective ways to show categorical proportions. Whether you are looking at the market share of tech giants or the population distribution across states, Python makes … Read more >>
I have spent years building data visualizations and dashboards using Python. Over time, I have found that the standard pie chart often feels cluttered and outdated. When I want to create a cleaner, more modern look for my reports, I always reach for the Python Matplotlib donut chart. It is essentially a pie chart with … Read more >>
I’ve found that standard classification isn’t always enough. Sometimes, you need to know how “close” two images are rather than just labeling them. Metric learning allows us to train a model to map images into a multi-dimensional space where similar items sit close together. This is the secret sauce behind facial recognition and product recommendation … Read more >>
Have you ever found yourself staring at thousands of unsorted product photos or medical images, wondering how to organize them? I remember the first time I faced this challenge during a large-scale project; I tried manual sorting until I realized Keras could do the heavy lifting for me. Semantic image clustering isn’t just about grouping … Read more >>
When I first started working with self-supervised learning, I found that standard contrastive methods often relied too heavily on simple data augmentations to create “positive” pairs. I discovered that Nearest Neighbor Contrastive Learning (NNCLR) changes the game by using the nearest neighbor in a support set as a positive sample, which offers much more semantic … Read more >>
Finding similar images is a task I have faced many times while building recommendation engines for e-commerce platforms. Standard classification often falls short when you need to compare how “alike” two distinct objects are. In this tutorial, I will show you how to use TensorFlow Similarity to perform metric learning, allowing your models to learn … Read more >>
I have spent over a decade writing Python code, and if there is one task I perform daily, it is searching for specific text within a larger body of data. Whether I am filtering through US Census Bureau datasets or parsing address strings from a California real estate database, checking for a substring is a … Read more >>
I have spent over a decade writing Python code for various engineering projects across the United States. Calculating the area of a square is one of the most fundamental tasks you will encounter when starting with Python geometry. Whether you are building a real estate app for Texas ranch sizes or a simple flooring calculator … Read more >>