VGG From Scratch – Deep Learning Theory & PyTorch Implementation (Full Course)

This course is a hands-on deep learning tutorial that will help you understand one of the most influential convolutional neural networks in computer vision. You will learn to rebuild the VGG architecture from the ground up while mastering the theory, mathematics, and design principles that shaped it. VGG stands for Visual Geometry Group. It is a deep convolutional neural network architecture known for its simple, uniform use of small 3x3 filters stacked in sequence, enabling powerful image recognition and feature extraction. Course created by @programmingoceanacademy 💻 Code: https://github.com/MOHAMMEDFAHD/pytorch-collections/blob/main/building_computer_vision_tiny_VGG_model_image_classification_problem.ipynb Resources: · https://www.programming-ocean.com/knowledge-hub/vgg-architecture-ai.php · https://www.programming-ocean.com/knowledge-hub/data-augmentation-atlas.php ❤️ Try interactive Python courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Python (Made possible by a grant from our friends at Scrimba) ⭐️ Contents ⭐️ 0:00:00 Welcome & Overview of the VGG Atlas 0:09:38 Philosophy Behind VGG: Depth with Simplicity 0:10:29 Historical Origins & Architectural Motivation 0:17:10 Mathematics of Convolution in VGG 0:20:25 Design Principles: Uniformity & Depth 0:23:22 Peer Comparison: VGG vs Contemporary Architectures 0:28:25 Training Strategy: Optimizing the VGG Model 0:42:33 Exploring Data Augmentation Techniques 0:49:56 VGG in Transfer Learning Applications 1:03:57 Visualization & Interpretability Techniques 1:14:10 VGG Variants: A Family of Deep Nets 1:16:46 Hands-on Walkthrough: Practical Applications 1:18:02 VGG Ecosystem & Research Resources 1:19:45 Kicking Off Practical Labs in Google Colab 1:21:07 Setting Up Your Coding Environment 1:23:36 Tiny VGG: Building the Model from Scratch 1:25:34 Importing Essential Libraries 1:29:54 Loading and Preparing Data in Google Colab 1:41:16 Familiarizing with Data Folders and Fi