Скачать или смотреть 🔍 VGG – Replicating Structure from Scratch using PyTorch (From Theory to Implementation)

Welcome to VGG Atlas, a complete deep learning journey where you’ll learn not just how to use VGG — but how to understand, build, and train it from the ground up using PyTorch and Google Colab.

This hands-on tutorial takes you from the philosophical foundations of the VGG architecture to a fully working Tiny VGG model built from scratch, with step-by-step explanations, visualizations, and best practices in model training and evaluation.

Whether you're a beginner looking to grasp CNN architecture or an intermediate learner seeking to deepen your PyTorch skills, this is a full-course experience in a single video.

🧠 What You’ll Learn:
The philosophy, design principles, and origins of VGG

Mathematical formulation of convolution

Step-by-step PyTorch model construction

Real-world image data handling, transformation, and visualization

Training, testing, loss plotting, and model fine-tuning

A Colab-ready code walkthrough that works seamlessly

📁 Resources:
🔗 GitHub Code: https://github.com/MOHAMMEDFAHD/vgg-a...
📑 Colab Notebook: (Link coming soon)
📷 Visual Tools: CNN Explainer, torchinfo, matplotlib

⏱️ Timestamps:
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تحرير
0:00:00 – Welcome & Overview of the VGG Atlas
0:08:18 – Philosophy Behind VGG: Depth with Simplicity
0:09:09 – Historical Origins & Architectural Motivation
0:15:50 – Mathematics of Convolution in VGG
0:19:05 – Design Principles: Uniformity & Depth
0:22:02 – Peer Comparison: VGG vs Contemporary Architectures
0:27:05 – Training Strategy: Optimizing the VGG Model
0:41:13 – Exploring Data Augmentation Techniques
0:48:36 – VGG in Transfer Learning Applications
1:02:37 – Visualization & Interpretability Techniques
1:12:50 – VGG Variants: A Family of Deep Nets
1:15:26 – Hands-on Walkthrough: Practical Applications
1:16:42 – VGG Ecosystem & Research Resources
1:18:25 – Kicking Off Practical Labs in Google Colab
1:19:47 – Setting Up Your Coding Environment
1:22:16 – Tiny VGG: Building the Model from Scratch
1:24:14 – Importing Essential Libraries
1:28:34 – Loading and Preparing Data in Google Colab
1:39:56 – Familiarizing with Data Folders and Files
1:46:06 – Setting Up the Directory Path for Data
1:46:36 – Becoming One with the Data
2:00:44 – Visualizing Sample Images with Metadata
2:01:24 – Visualizing Images in Python Using NumPy and Matplotlib
2:07:44 – Transforming the Data
2:11:34 – Visualizing Transformed Data with PyTorch
2:15:14 – Transforming Data with `torchvision.transforms`
2:22:20 – Loading Data Using `ImageFolder`
2:52:20 – Turning Loaded Images into a DataLoader
3:07:00 – Visualizing Some Sample Images
3:08:22 – Starting VGG Model Construction & Explaining Structure Using CNN Explainer Tool
3:18:55 – Replicating the CNN Explainer Tool VGG Model in Google Colab Using Code
3:50:25 – Instantiating an Instance from the VGG Model
3:55:01 – Displaying and Summarizing the VGG Model
3:55:41 – Dummy Forward Pass Using a Single Image
4:06:40 – Using `torchinfo` to Understand Input/Output Shapes in the Model
4:08:53 – Model Summary
4:18:53 – Creating the Training and Testing Loop
4:40:13 – Creating a Function to Combine Training and Testing Steps
4:50:09 – Calling the Training Function
5:02:45 – Training the Model: Running the Training Step
5:02:55 – Reading the Results, Fine-Tuning, and Improving Hyperparameters
5:10:45 – Plotting the Loss Curve and Fine-Tuning with Different Settings
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