I have spent the last four years building deep learning models, and if there is one thing I have learned, it is that standard Convolutional Neural Networks (ConvNets) sometimes miss the “big picture.” While convolutions are great at picking up local patterns, they often struggle to understand which parts of an image are truly important … Read more >>
I’ve found that scaling Vision Transformers (ViT) often leads to significant training instability. Standard ViT architectures tend to saturate or diverge when you add too many layers, which can be quite frustrating during model development. Recently, I started using Class Attention Image Transformers (CaiT), which introduces LayerScale to handle these deep architectural challenges effectively. In … Read more >>
I have often noticed a frustrating drop in accuracy when deploying models. You train a model on $224 \times 224$ images, but the real-world performance only peaks when you feed it larger images during inference. This phenomenon is known as the train-test resolution discrepancy, and it occurs because the statistics of the data change when … Read more >>
I have spent a significant amount of time building complex deep learning models that perform brilliantly but are far too heavy for mobile devices. In my experience, Knowledge Distillation is the most effective way to shrink a massive “Teacher” model into a compact “Student” model while keeping the accuracy high. In this tutorial, I will … Read more >>
In my four years of working with Keras, I’ve realized that moving from traditional CNNs to Vision Transformers (ViT) is a massive shift. The most confusing part for many developers I mentor is how we actually turn a standard image into a sequence of tokens that a Transformer can understand. In this tutorial, I will … Read more >>
In my years of working with deep learning, I have often hit a wall where my models just wouldn’t converge fast enough. I used to spend hours tweaking learning rates, only to find that the weight gradients were becoming unstable during backpropagation. Then I discovered Gradient Centralization (GC), a simple yet powerful technique that operates … 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’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 >>
I’ve found that traditional classification often fails when you have thousands of categories but very few images for each. I remember building a facial recognition system for a small tech firm, where we couldn’t possibly retrain the model every time a new employee was hired. That is where Siamese Networks saved my project, as they … Read more >>
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