Torchtext offers a wide range of pre-processed datasets commonly used in natural language processing (NLP) research and applications. By having a comprehensive list of available datasets, users can quickly identify...

Torchaudio provides a collection of datasets, which can be incredibly useful for several reasons. It can be used for training and evaluation of audio models. The datasets serve as a...

Obtaining a list of all available datasets in Torchvision can be useful for researchers, practitioners, and enthusiasts in the field of computer vision. It can help to identify suitable datasets...

If you're starting to work with deep learning and using TensorFlow as your framework of choice, it can be useful to know of the available GPU devices on your system...

PyTorch provides support for utilizing Graphics Processing Units (GPUs) to accelerate computations and improve training times for deep learning models. Obtaining available GPU devices can be useful for identifying and...

When working with image processing tasks in Python, Pillow (PIL) and the scikit-image library are two powerful tools that can help you manipulate and analyze images. While PIL is widely...

The scikit-image and Pillow (PIL) are two popular Python libraries widely used in image processing tasks. While scikit-image provides a rich set of functionalities for manipulating and analyzing images, PIL...

When working with computer vision tasks, we often need to visualize the TensorFlow tensors as images. Here the Pillow (PIL) library comes in useful. By converting TensorFlow tensor to PIL...

Python provides several powerful libraries for image processing, such as Pillow (PIL). While PIL offers many functions to work with images, TensorFlow provides a comprehensive ecosystem for building and training...

When working with computer vision and deep learning tasks, TensorFlow and OpenCV are two powerful libraries that often go hand in hand. In certain scenarios, you may need to convert...