Introduction to Pytorch for Engineers

This course provides a hands-on introduction to PyTorch, a powerful deep learning framework, tailored for engineers. Participants will learn the essentials of PyTorch, from building and training models to leveraging pre-trained architectures. Key topics include handling datasets, creating convolutional neural networks, and applying data augmentation techniques. The course emphasizes practical applications, providing foundational knowledge and best practices for deep learning projects.

Before the Course

The provided notebooks are tested with Python 3.10. All required libraries are listed in requirements.txt. You can install them as follows:

pip install -r requirements.txt

Before diving into PyTorch, ensure you have a basic understanding of Python. Check out the following notebook:

0. Prerequisites - What You Should Know Before You Start

Course Content

1. Introduction to PyTorch - Why It's So Great

   • In this section, we'll introduce PyTorch and highlight its strengths.

2. Models and Training - Training and Validating a Simple Classification Model

   • Learn how to train and validate a simple classification model.

3. Datasets and Dataloaders - Training a Model with Dataset and Dataloader

   • Understand how PyTorch handles data through Dataset and DataLoader classes.

4. Convolutional Neural Networks - Understanding and Applying the Basics

   • Explore practical tricks and best practices for working with convolutional layers effectively.

5. Using Popular Pre-trained Models - Importing Pre-trained Models or Backbones from Torchvision

   • Explore how to leverage pre-trained models or use backbone architectures from Torchvision.

6. Data Augmentation - Because More is Better

   • Learn the importance of data augmentation in improving model generalization.