Implementing Residual Networks from Scratch to training

Building and Training ResNet Models in PyTorch – A Complete Guide

Created byEzeuko Emmanuel

Last updated 8/2025

English

What you'll learn

Implement custom Residual Blocks and ResNet architectures (ResNet50, ResNet101, ResNet152) from scratch using PyTorch.
Prepare and preprocess image datasets with normalization, resizing, data augmentation, and efficient DataLoader creation.
Write and optimize complete training and validation loops including loss calculation, accuracy measurement, and GPU acceleration.
Compare and analyze model performance with and without residual connections to understand their effect on deep network training.

Course content

1 section • 11 lectures • 2h 5m total length

1. convolution neural networks explained12:57
2. Residual neural network explained19:35
3. Architecture of RNN16:09
4. How a ResidualBlock Transforms Input Tensors — PyTorch Demo14:51
5. Deep Dive into Residual Blocks : Implementing Residual Blocks in PyTorch19:55
6. Implementing Residual Block Sequences with Downsampling in PyTorch9:18
7. How ResNet Processes Data: Step-by-Step Forward Method Explained2:34
8. Step-by-Step Guide to Dataset Loading and Augmentation with PyTorch10:50
9. Training Deep Learning Models in PyTorch: Step-by-Step Guide9:10
10. Training ResNet Models on GPU: Residuals vs No Residuals Explained4:01
11. setting up kaggle dataset and visualizing graph6:27

Requirements

python

Description

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.

Who this course is for:

all levels python programmers