Building Networks with PyTorch

PyTorch is the most popular deep learning framework in research and is increasingly adopted in production. Developed by Meta AI, it provides a flexible, Pythonic interface for building, training, and deploying neural networks.

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Why PyTorch?

Feature	Description
Dynamic computation graphs	Build-by-run approach — the graph is constructed on the fly during execution
Pythonic API	Feels like writing standard Python; easy to debug with print statements and breakpoints
Strong GPU support	Seamless tensor operations on GPU with .to('cuda')
Rich ecosystem	torchvision, torchaudio, torchtext, Hugging Face integration
Research dominance	Used in the majority of NeurIPS, ICML, and ICLR papers
Production ready	TorchScript, TorchServe, ONNX export for deployment

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Tensors — The Core Data Structure

Tensors are the fundamental data structure in PyTorch — multi-dimensional arrays similar to NumPy arrays but with GPU support and automatic differentiation.

import torch