Bijay Kumar 
I have built dozens of React applications, and I often found myself copying the same UI buttons and input fields from one project to another. It was a huge waste of time. I finally decided to pack my reusable UI elements into a standalone library that I could install via NPM in seconds. In this … Read more >>
Managing data is at the heart of almost every React application I have built over the last eight years. Whether I was working on a financial dashboard for a New York hedge fund or a logistics tracker for a shipping company in Chicago, tables were always the centerpiece. In this tutorial, I will show you … Read more >>
Have you ever found yourself staring at thousands of customer support tickets or legal documents, wondering how to categorize them automatically? In my four years as a Keras developer, I’ve realized that assigning just one category to a text is rarely enough for complex, real-world data. Most documents belong to multiple topics at once, such … Read more >>
I’ve spent the last four years building deep learning models, and one thing I’ve realized is that Transformers can be quite heavy on resources. Recently, I’ve been experimenting with FNet, a model that replaces the complex self-attention layer with a simple Fourier Transform. In this guide, I’ll show you exactly how I use FNet for … Read more >>
Have you ever built a sentiment analysis model, only to realize you have thousands of reviews but zero labels? It is a common headache I have faced many times while working on large-scale Python Keras projects for retail clients. Manually labeling 10,000 reviews is not just boring; it is a massive waste of your technical … Read more >>
I have spent years building computer vision pipelines, and I can tell you that YOLOv8 combined with KerasCV is a game-changer. It simplifies the process of building high-performance models while keeping your code clean and readable. In this tutorial, I will show you exactly how to implement object detection. We will move away from generic … Read more >>
When I first started training deep learning models for image classification, I often relied on the standard Cross-Entropy loss. It worked well enough, but I noticed the models struggled when the classes were visually similar. I discovered that Supervised Contrastive Learning (SupCon) is a game-changer for these scenarios. It helps the model learn to pull … Read more >>
I’ve often found that the best way for a model to “learn” an image is by trying to fix a broken one. Masked Image Modeling (MIM) is a fascinating technique where we intentionally hide parts of an image and ask an Autoencoder to reconstruct the missing pieces. I remember the first time I applied this … Read more >>
During my decade of working with Python, I’ve often had to break down large strings into manageable chunks. Whether I was processing social security numbers or formatting large batches of California zip codes, this task comes up more often than you’d think. In this tutorial, I’ll show you the most efficient ways to split a … Read more >>
In my years of developing Python applications, I’ve often come across situations where the standard split() method just doesn’t cut it. Sometimes, you don’t have a delimiter like a comma or a space to rely on. You simply need to cut a string at a specific position. Whether you are parsing fixed-width data files or … Read more >>
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