PyTorch Fundamentals 🚀

A quick beginner-friendly guide to PyTorch basics: tensors, math operations, reshaping, NumPy interoperability, random seeds, and GPU usage.

Key Concepts

• Scalars / Vectors / Matrices / Tensors → torch.tensor()
• Create tensors → torch.rand(), torch.zeros(), torch.ones(), torch.arange()
• Data types & devices → .type(), requires_grad=True, .to(device)
• Math operations → +, -, *, /, torch.matmul()
• Matrix operations → .mm(), .T, .reshape(), .view(), torch.stack()
• Dimension operations → .squeeze(), .unsqueeze(), .permute()
• NumPy ↔ PyTorch → torch.from_numpy(), .numpy()
• Random seeds → torch.manual_seed()
• GPU / CUDA → .cuda(), torch.cuda.is_available(), .cpu()

Quick Start

pip install torch torchvision torchaudio

Feedback

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