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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 >>
I still remember the panic I felt during a database migration for a large US healthcare provider when a “Zip Code” column caused the entire pipeline to crash. It was a rookie mistake that taught me a valuable lesson about Python’s dynamic typing system: never assume your data is what you think it is. I … Read more >>
I still remember the first time I spent three hours debugging a production script, only to realize a logical flag I thought was a True boolean was actually the string “True”. It’s a classic mistake that even seasoned developers make, especially when dealing with data coming from APIs or user inputs. Python is dynamically typed, … Read more >>
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