Implementing Residual Networks from Scratch to training

This hands-on course takes you step-by-step through building, understanding, and training Residual Networks (ResNet) in PyTorch entirely from scratch. You will not only learn how ResNet works under the hood but also gain practical skills to implement and customize it for real-world image classification tasks. By the end of the course, you will:Understand the concept of Residual Blocks and why skip connections solve the vanishing gradient problem.

Implement custom ResNet architectures (ResNet50, ResNet101, ResNet152) directly in PyTorch without relying on pre-built libraries. Prepare and preprocess image datasets (Cats vs Dogs) using transforms, normalization, and data augmentation. Split datasets into training and validation sets and create efficient DataLoaders.

Write a full model training loop from scratch, including forward propagation, backpropagation, loss calculation, and optimizer updates. Evaluate model performance with accuracy and loss tracking for both training and validation. Compare training results with and without residual connections to see their impact on performance.

Utilize GPU acceleration in PyTorch for faster model training. Whether you are a beginner looking to understand convolutional neural networks or an intermediate learner aiming to deepen your PyTorch skills, this course will equip you with both the theory and coding expertise needed to implement advanced deep learning models.