As a Python Keras developer with over four years of experience, I’ve seen how sequence modeling has evolved dramatically. Recurrent Neural Networks (RNNs) have long been the go-to for time series and sequential data, but transformers have brought a revolution with their attention mechanisms. Recently, I explored how combining recurrence with transformers can lead to … Read more >>
Image classification is a fundamental task in computer vision, and with the rise of transformer architectures, the Global Context Vision Transformer (GC ViT) has emerged as a powerful alternative to traditional CNNs. Drawing from my 4+ years of experience with Python Keras, I will guide you step-by-step on how to implement image classification using GC … Read more >>
Vision Transformers (ViTs) have revolutionized computer vision by leveraging the power of transformers. Traditionally, ViTs rely heavily on the attention mechanism to capture relationships within image patches. But what if we could simplify this architecture by removing the attention component altogether? In this article, I’ll share my firsthand experience building a Vision Transformer without attention … Read more >>
When I first started exploring Vision Transformers (ViT) in Keras, I was amazed by their performance on large datasets like ImageNet. However, training these models on small datasets can be tricky. From my experience, with the right approach and some tweaks, you can successfully train a ViT for image classification even when the data is … Read more >>
As a Python Keras developer with over four years of experience, I’ve seen how Transformer models revolutionized natural language processing and are now reshaping computer vision tasks. Among these, the Swin Transformer stands out for its ability to efficiently capture both local and global image features, making it a powerful tool for image classification. In … Read more >>
I’ve spent years working with Keras to build efficient deep learning models, and one challenge I often face is training image classifiers with limited labeled data. Semi-supervised learning, combined with contrastive pretraining, offers a powerful solution. In this tutorial, I’ll walk you through how to use SimCLR, a popular contrastive learning framework, to pretrain your … Read more >>
Recently, I was working on a project where I had to train a model to recognise new categories with very little data. The issue is that deep learning models usually need thousands of examples to learn anything useful. So we need a workaround that allows our model to learn fast, just like a human can … Read more >>
I remember debugging a data pipeline for a large logistics company. The system was crashing randomly, throwing type errors that made no sense at first glance. After hours of digging, I realized the issue wasn’t the logic; it was the data types. Some legacy sensors were sending raw binary data (bytes), while newer APIs were … Read more >>
I still remember the first time I deployed a large-scale data processing pipeline for a major logistics company based in Chicago. We were processing millions of shipping addresses, and the entire system crashed because I hadn’t properly accounted for missing data in the “State” field. It turns out that checking for an empty string in … Read more >>
I still remember the late night I spent debugging a payroll system for a client in Chicago because of a single missing value. The system crashed because I treated an empty string from a database field the same way as a Python None object. I have learned that “nothing” comes in many different shapes and … Read more >>
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