Zero to GAN

Learn PyTorch and Deep Learning step by step, from basic tensor operations to implementing GANs.

Prerequisites

• Python 3.x
• PyTorch
• Basic understanding of Python programming
• Familiarity with linear algebra and calculus concepts

Running the Notebooks

Each course contains Jupyter notebooks with detailed explanations and code examples. To get started:

1. Clone this repository
2. Install the required dependencies
3. Open the notebooks in Jupyter Lab/Notebook
4. Follow along with the examples and exercises

Course Contents

Course 1: Basics & Gradient Descent

Learn the fundamentals of PyTorch, including:

• Tensor operations and manipulations
• Automatic differentiation (Autograd)
• Implementing linear regression from scratch
• Using PyTorch's built-in modules

Course 2: Image Classification

Learn about working with image data and building CNNs:

• Working with image datasets
• Data normalization and augmentation
• Building Convolutional Neural Networks
• Training and evaluating image classifiers

License

MIT License - feel free to use this material for learning purposes.

Resources

• https://jovian.com/learn/deep-learning-with-pytorch-zero-to-gans