PyTorch Tutorials 🚀

Welcome to the PyTorch Tutorials Repository! This repository contains a collection of Jupyter Notebook tutorials that cover various aspects of PyTorch, a popular open-source machine learning framework. Each tutorial is designed to help you grasp fundamental concepts and techniques using PyTorch. The notebooks include explanations, code examples, and links to the full tutorials available on my website.

Feel free to explore and learn from the tutorials listed below:

• PyTorch Tensors: Get hands-on experience with PyTorch tensors in this tutorial. Learn about tensor creation, manipulation, and mathematical operations.
• PyTorch transforms: Image transformations are crucial in data augmentation for computer vision tasks. This tutorial covers how to use PyTorch's transformation capabilities effectively.
• PyTorch AutoGrad: This tutorial introduces you to PyTorch's automatic differentiation package, autograd, which enables computing gradients automatically for tensor operations.
• CNNs in PyTorch: Dive into Convolutional Neural Networks (CNNs) with this tutorial. Learn how to build and train CNNs for image classification tasks using PyTorch.
• Linear Regression in PyTorch: Understand the basics of linear regression using PyTorch tensors. This tutorial walks you through creating a simple linear regression model from scratch.

For the full tutorials, detailed explanations, and additional resources, please visit datagy.io. Each tutorial notebook contains links that will take you directly to the corresponding tutorial on the website.

Getting Started

To get started with these tutorials, follow these steps:

1. Clone this repository to your local machine using the following command:

   git clone https://github.com/nik-pi/PyTorch-Tutorials
   

2. Install the required dependencies. You can create a virtual environment and install the dependencies using the provided requirements.txt file:

   cd pytorch-tutorials
   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   

3. Launch Jupyter Notebook:

   jupyter notebook
   

4. Navigate to the tutorial notebooks (e.g., PyTorch Autograd.ipynb) and start learning!

Happy learning! 👋🏼