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

This people is simply a hands-on heavy learning tutorial that will thief you understand 1 of the astir influential convolutional neural networks successful machine vision. You will study to rebuild the VGG architecture from the crushed up while mastering the theory, mathematics, and creation principles that shaped it. VGG stands for Visual Geometry Group. It is simply a heavy convolutional neural web architecture known for its simple, azygous usage of mini 3x3 filters stacked successful sequence, enabling powerful image nickname and characteristic 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, correct successful your browser: https://scrimba.com/freeCodeCamp-Python (Made imaginable by a assistance from our friends astatine Scrimba) ⭐️ Contents ⭐️ 0:00:00 Welcome & Overview of the VGG Atlas 0:09:38 Philosophy Behind VGG: Depth pinch Simplicity 0:10:29 Historical Origins & Architectural Motivation 0:17:10 Mathematics of Convolution successful 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 successful 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 successful 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 successful Google Colab 1:41:16 Familiarizing pinch Data Folders and Files 1:47:26 Setting Up the Directory Path for Data 1:47:56 Becoming One pinch the Data 2:02:04 Visualizing Sample Images pinch Metadata 2:02:44 Visualizing Images successful Python Using NumPy and Matplotlib 2:09:04 Transforming the Data 2:12:54 Visualizing Transformed Data pinch PyTorch 2:16:34 Transforming Data pinch `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 successful 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 successful 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 pinch 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 codification for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles connected programming: https://freecodecamp.org/news