PyTorch is more or less similar to TensorFlow except that it is fully wired for building deep learning models. Also an open-source AI-driven machine learning platform, it was developed in 2016 by Facebook’s AI Research (FAIR) team, and has since gained significant popularity in the research and development communities for providing a flexible and efficient framework for building and training deep learning models.

Its extensive support for tensor computations makes it well-suited for tasks involving numerical data. The platform allows seamless integration with GPUs, enabling efficient training and inference on accelerated hardware. It also includes automatic differentiation, a critical feature for optimizing models through techniques such as gradient descent.

Users get access to a library of tools and features that complement its main functionality. For example, torchVision provides pre-trained models and datasets for computer vision tasks, while torchtext focuses on natural language processing. It also supports deployment on mobile and embedded platforms through TorchServe and TorchScript, enabling model deployment beyond traditional computing environments.

Being an open-source platform, PyTorch reinforces a strong community presence and a vibrant research community that allows collaboration and knowledge sharing. This makes it a flexible and powerful platform to breathe life into fresh ideas, for both beginners and experienced developers.