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VGGNet: The legendary paper with 145k citations threatened the dominance of AlexNet| Computer Vision

Full code: https://colab.research.google.com/dri... Miro board: https://miro.com/app/board/uXjVI3a-yW... ***** 🎓 VGGNet: Why a Simple Idea Took Over Deep Learning In this lecture, we explore a model that quietly reshaped the deep learning landscape: VGGNet. It did not introduce radical new layers. It did not promise to solve every task. But it taught us a crucial lesson: Depth + simplicity = power. VGGNet replaced large filters with stacks of 3×3 convolutions, delivering stronger representations with fewer parameters and more non-linearities. This subtle shift created a ripple effect across nearly every CNN architecture that followed. 🔍 In this video, you’ll learn: Why stacking 3×3 filters works so well What makes VGGNet’s architecture so elegant How to use a pretrained VGG model for transfer learning When deeper models hurt more than help Why your validation accuracy can outperform training accuracy — and why that’s OK We’ll walk through both theory and code, using the 5-Flowers dataset as our testbed. 📊 What’s surprising? In our experiment, AlexNet actually beat VGG on this small dataset. Why? Because size is not the only thing that matters. We’ll explore what this means and how to make thoughtful model choices. 💻 Includes: VGG block implementation Transfer learning setup in PyTorch Architecture diagrams Practical conclusions from real training results #VGGNet #DeepLearning #ComputerVision #CNN #PyTorch #MachineLearning #Vizuara