Welcome to the PyTorch Basics guide! Whether you're new to deep learning or just starting with PyTorch, this tutorial will help you get familiar with the essentials. Let's dive in!

Installation 🛠️

To begin, install PyTorch using pip or conda. 

pip install torch

📌 Tip: For GPU support, use pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118

PyTorch Installation

Core Concepts 🧠

  1. Tensors - Multi-dimensional arrays similar to NumPy's ndarray.
    ⚠️ Example: torch.tensor([[1, 2], [3, 4]])
  2. Autograd - Automatic differentiation for tracking operations.
    🔍 Key feature: Gradient computation via backward() method
  3. nn.Module - Neural network building blocks for models.
Tensor Basics

Quick Start Example 📈

import torch
x = torch.tensor(3.0)
y = x**2
dy_dx = y.backward()  # Compute derivative
print(f"Derivative of y = x² at x=3 is {dy_dx}")

🎉 Output: Derivative of y = x² at x=3 is 6.0

For more advanced topics, check out our PyTorch Advanced Tutorial.
Let me know if you need help with anything else! 😊