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 Files 1:47:26 Setting Up the Directory Path for Data 1:47:56 Becoming One with the Data 2:02:04 Visualizing Sample Images with Metadata 2:02:44 Visualizing Images in Python Using NumPy and Matplotlib 2:09:04 Transforming the Data 2:12:54 Visualizing Transformed Data with PyTorch 2:16:34 Transforming Data with `torchvision.transforms` 2:23:40 Loading Data Using `ImageFolder` 2:53:40 Turning Loaded Images into a DataLoader 3:08:20 Visualizing Some Sample Images 3:09:42 Starting VGG Model Construction & Explaining Structure Using CNN Explainer Tool 3:20:15 Replicating the CNN Explainer Tool VGG Model in Google Colab Using Code 3:51:45 Instantiating an Instance from the VGG Model 3:56:21 Displaying and Summarizing the VGG Model 3:57:01 Dummy Forward Pass Using a Single Image 4:08:00 Using `torchinfo` to Understand Input/Output Shapes in the Model 4:10:13 Model Summary 4:20:13 Creating the Training and Testing Loop 4:41:33 Creating a Function to Combine Training and Testing Steps 4:51:29 Calling the Training Function 5:04:05 Training the Model: Running the Training Step 5:04:15 Reading the Results, Fine-Tuning, and Improving Hyperparameters 5:12:05 Plotting the Loss Curve and Fine-Tuning with Different Settings 🎉 Thanks to our Champion and Sponsor supporters: 👾 Drake Milly 👾 Ulises Moralez 👾 Goddard Tan 👾 David MG 👾 Matthew Springman 👾 Claudio 👾 Oscar R. 👾 jedi-or-sith 👾 Nattira Maneerat 👾 Justin Hual -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news