PyTorch for Deep Learning and Computer Vision Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
This course provides a hands-on introduction to PyTorch for deep learning and computer vision, designed for beginners with basic Python and neural network knowledge. You'll progress from PyTorch fundamentals to building and deploying convolutional neural networks using real-world image datasets. The curriculum spans approximately 10 hours of content, combining theory, coding exercises, and a final project to solidify your skills in modern deep learning workflows.
Module 1: Introduction to PyTorch & Deep Learning
Estimated time: 0.5 hours
- Overview of PyTorch ecosystem and installation
- Introduction to tensors and tensor operations
- Understanding gradients and autograd system
Module 2: Building Neural Networks
Estimated time: 1 hours
- Creating models using nn.Module and Sequential
- Defining layers, parameters, and forward pass
- Selecting loss functions and optimizers
Module 3: Training Deep Neural Networks
Estimated time: 1 hours
- Building training loops with dataloaders
- Implementing evaluation cycles and metrics
- Saving, loading, and reusing trained models
Module 4: Computer Vision with CNNs
Estimated time: 1.25 hours
- Building CNNs from scratch for image classification
- Applying convolution, pooling, and flattening layers
- Designing architectures for spatial feature extraction
Module 5: Famous Architectures in PyTorch
Estimated time: 1.5 hours
- Recreating LeNet and AlexNet in PyTorch
- Implementing VGG and ResNet architectures
- Adapting pretrained models to new tasks
Module 6: Final Project
Estimated time: 1.25 hours
- End-to-end image classifier pipeline
- Model export for inference
- Deployment in real-world environments
Prerequisites
- Familiarity with Python programming
- Basic understanding of neural networks
- Some experience with machine learning concepts
What You'll Be Able to Do After
- Build and train deep learning models using PyTorch
- Implement CNNs for computer vision tasks from scratch
- Work with standard and custom image datasets
- Apply transfer learning and fine-tuning techniques
- Deploy trained models into production-like environments