Pytorch Beginners Guide

Welcome to Pytorch-BeginnersGuide — a curated set of notebooks designed to help you get started with PyTorch, one of the most popular deep learning frameworks. Whether you're new to machine learning or transitioning from another library, this guide walks you through the essentials with real code and datasets.

🚀 What You'll Learn

• Tensor operations and broadcasting
• Building neural networks with torch.nn
• Activation functions: Sigmoid, ReLU, etc.
• Optimizers and training loops
• Using torch.utils.data and torchvision
• Hands-on examples: MNIST classification, Titanic survival prediction, and more

📁 Repository Structure

Pytorch-BeginnersGuide/
├── MNIST.py                      # MNIST digit classification
├── diabetes.csv.gz               # Dataset for regression/classification
├── ex02.py to ex08.py            # Progressive PyTorch exercises
├── titanic-0.1.py, titanic-0.2.py# Titanic survival prediction
├── gender_submission.csv         # Titanic sample submission
├── train.csv, test.csv           # Titanic datasets
├── names_train.csv.gz            # Name-based classification
├── 卷积-FromHadamardProductToLocalMatMul.py # Convolutional math demo
├── README.md                     # This file

📦 Requirements

• Python 3.8+
• PyTorch
• NumPy, Pandas, Matplotlib
• Jupyter Notebook or VSCode

Install dependencies:

pip install torch torchvision numpy pandas matplotlib

🧪 Getting Started

Open any .py or .ipynb file in your preferred IDE or notebook environment and run the cells step-by-step. Each script is self-contained and annotated for clarity.

🤝 Contributions

Feel free to fork, improve, or suggest new beginner-friendly modules. This guide is meant to grow with the community.