What is PyTorch used for?

PyTorch is an open-source machine learning framework used to build and train neural network models. It offers a variety of tools and functions to assist users in deep learning tasks such as image classification, object detection, semantic segmentation, natural language processing, etc. Key features of PyTorch include:

1. Tensor operations: PyTorch offers powerful tensor operations similar to NumPy arrays for efficient linear algebra, numerical computation, and array manipulation.
2. Automatic differentiation: The core of PyTorch is its automatic differentiation engine, which can automatically compute gradients of neural network models. This allows users to easily implement the backpropagation algorithm to update model parameters.
3. Neural Network Modules: PyTorch offers a variety of predefined modules, such as convolutional layers, pooling layers, recurrent neural networks, as well as various activation and loss functions.
4. Advanced Optimizers: PyTorch offers various advanced optimizers like stochastic gradient descent (SGD), Adam, Adagrad, etc., to help users effectively train neural network models.
5. PyTorch offers support for distributed training, allowing models to be trained simultaneously on multiple GPUs or machines to speed up the training process.

In conclusion, PyTorch is a powerful and flexible machine learning framework that can be used to build and train various deep learning models, and is widely used in practice.