PyTorch Main Components#
Created On: Aug 13, 2025 | Last Updated On: Aug 13, 2025
PyTorch is a flexible and powerful library for deep learning that provides a comprehensive set of tools for building, training, and deploying machine learning models.
PyTorch Components for Basic Deep Learning#
Some of the basic PyTorch components include:
Tensors - N-dimensional arrays that serve as PyTorch’s fundamental data structure. They support automatic differentiation, hardware acceleration, and provide a comprehensive API for mathematical operations.
Autograd - PyTorch’s automatic differentiation engine that tracks operations performed on tensors and builds a computational graph dynamically to be able to compute gradients.
Neural Network API - A modular framework for building neural networks with pre-defined layers, activation functions, and loss functions. The
nn.Modulebase class provides a clean interface for creating custom network architectures with parameter management.DataLoaders - Tools for efficient data handling that provide features like batching, shuffling, and parallel data loading. They abstract away the complexities of data preprocessing and iteration, allowing for optimized training loops.
