Welcome to the Pytorch course created by HDSP Research Group! In this hands-on course, you will learn the basics of Pytorch a powerful framework for building and training neural networks.
The entire course content is condensed into a single notebook:
The course material is contained within a notebook - classification_pytorch.ipynb. This notebook provides a hands-on walkthrough of PyTorch basics by stepping through the tasks of loading data, building and training a neural network model, and evaluating the results.
this material can be downloaded and run locally, or used directly on Google Colab
The sections in the notebook are divided as follows:
| Modules | Description |
|---|---|
| 1 | Data structure in Pytorch: Tensors |
| 2 | Data Loading and Visualization: CIFAR10 |
| 3 | Define a Convolutional Neural Network |
| 4 | Define a Loss function |
| 5 | Define a Optimizer |
| 6 | Pytorch Training Loop |
| 7 | Hyperparameter tunning |
Each section contains explanatory text, annotated code samples, and relevant images to provide an intuitive understanding