What is PyTorch?

This course builds upon knowledge of NumPy and basic understanding of machine learning and neural networks, that's why we strongly recommend completing the following courses beforehand (simply click on them to get started):

Created by Meta AI, it has quickly become a favorite among researchers and practitioners in the field of artificial intelligence (AI) and deep learning.

Applications of PyTorch

Deep Learning Research

PyTorch's flexibility and dynamic computation graph make it ideal for experimenting with novel architectures and advancing research in deep learning.

Natural Language Processing (NLP)

PyTorch powers tasks like text classification, machine translation, and sentiment analysis, leveraging state-of-the-art models like transformers.

Computer Vision

PyTorch is widely used for image classification, object detection, and image segmentation due to its rich library support and pre-trained models.

PyTorch vs TensorFlow

TensorFlow is another open-source machine learning framework developed by Google. Known for its scalability and production-ready features, TensorFlow has long been a preferred choice for deploying machine learning models in real-world applications.

However, PyTorch has gained rapid popularity due to its flexibility and ease of use, particularly in research and experimentation.

In summary, PyTorch has established itself as a leader in AI and deep learning by combining flexibility, ease of use, and strong community support. Its focus on research and production-readiness ensures it will remain a top choice for AI development in years to come.