PyTorch

TL;DR

• Flexible, easy-to-use deep learning framework with a dynamic computational graph that can be changed at runtime.
• Built-in GPU acceleration via CUDA for faster training on parallel hardware.
• Widely used for natural language processing (NLP), computer vision, and transfer learning workflows.

Definition

PyTorch is a popular open-source machine learning framework developed by Facebook’s AI Research team, primarily used for training deep learning models in natural language processing and computer vision. It is known for simplicity, flexibility, and ease of use.

Explanation

PyTorch constructs a dynamic computational graph on-the-fly, meaning the graph that represents mathematical operations is built at runtime and can be modified during execution. This provides flexibility when altering model structure or control flow compared with frameworks that require a statically defined graph before running (for example, TensorFlow as referenced in the source).

PyTorch also supports GPU acceleration. GPUs (graphics processing units) are specialized hardware for parallel computations; PyTorch provides built-in support for CUDA (a parallel computing platform from NVIDIA), enabling models to train and run much faster than on CPUs alone. These features make PyTorch practical for training large or complex models within reasonable timeframes.

Examples

Transfer learning

Transfer learning uses a pre-trained model on a new task to reduce required data and computation. In PyTorch, transfer learning is straightforward: you can fine-tune a pre-trained model by adding or removing layers and changing the computational graph as needed.

Image classification

Image classification—training a model to recognize and classify objects in images—can be computationally intensive for large datasets. PyTorch’s GPU support allows training such models much faster, making it feasible to use larger datasets and more complex models.

Use cases

• Natural language processing (NLP)
• Computer vision
• Transfer learning
• Image classification
• Training deep learning models

Related terms

• TensorFlow
• CUDA
• GPU
• Transfer learning
• Natural language processing (NLP)
• Computer vision
• Deep learning