PyTorch is an open-source machine learning library based on the Torch library, primarily developed by Facebook's artificial intelligence research group. It provides a Python interface for building and training neural networks and has gained popularity due to its dynamic computational graph, which allows for easier debugging and faster development. PyTorch also offers support for GPUs, making it ideal for deep learning tasks.
PyTorch provides a range of tools for building and training neural networks, including pre-built modules for commonly used layers, loss functions, and optimization algorithms. It also includes a range of tools for data preprocessing and visualization, making it easy to work with datasets.
One of the key features of PyTorch is its use of dynamic computational graphs. Unlike other deep learning frameworks, where the computational graph is defined statically, PyTorch allows the user to change the graph on the fly during training. This makes it easier to experiment with different network architectures and adjust the model as needed.
PyTorch also supports distributed computing, allowing users to train models across multiple GPUs or even multiple machines. This makes it a powerful tool for large-scale deep learning tasks.
Overall, PyTorch is a versatile and powerful machine learning library that offers a range of tools for building and training neural networks.