PyTorch Introduction

This repository contains the code for the PyTorch Introduction course. All code has been written along with the course. This is also my first formal introduction to the Python programming language, so I will be learning both PyTorch and Python at the same time. The topics covered in this course are:

• 00: Fundamentals
• 01: PyTorch Workflow
• 02: Classification Models (Binary & Multiclass)
• 03: Computer Vision
• 04: Custom Datasets & DataLoaders
• 05: Going Modular
• 06: Transfer Learning
• 07: Experiment Tracking
• 08: PyTorch Paper Replication
• 09: Model Deployment

About the Course

The course is by Daniel Bourke and is roughly 25 hours (+20 hours paid) long. Most of it can be found for free on YouTube. The following are some resources that are used throughout the course.

1. YouTube Video Tutorial: This video tutorial offers a visual walkthrough of PyTorch concepts, making it easier to grasp key concepts.

2. Learn PyTorch: This comprehensive online tutorial provides a step-by-step guide to learning PyTorch, starting from the basics and progressing to more advanced topics. This follows the same structure as the course video, making it a great resource to supplement my learning.

3. PyTorch Deep Learning GitHub Repository: This repository includes setup instructions and code examples for deep learning using PyTorch. It's a practical resource to apply your learning.

Other Helpful Resources

Throughout the course, I used the following resources to help me with my learning:

1. Introduction to Using Conda in VSCode: This LinkedIn article provides a clear introduction to setting up Conda environments within VSCode for Python development.

2. Introduction to Python and Conda in VSCode: This follow-up article delves deeper into using Python and Conda in VSCode, helping you make the most out of these tools.

3. TensorFlow Playground: This interactive website allows you to visualize the effects of different hyperparameters on a neural network. It's a great way to get a feel for how neural networks work.

4. PyTorch Documentation: This is the official documentation for PyTorch. It's a great resource to refer to when you're stuck on a particular concept, or if you want to learn more about a particular function.

5. CNN Explainer This interactive website allows you to visualize the effects of different hyperparameters on a convolutional neural network. It's a great way to get a feel for how convolutional neural networks work.