To deploy PyTorch models on Arm edge devices, you need to optimize the model, prepare the software, and use the right hardware. These steps help you deploy AI applications at the edge.

Why PyTorch? PyTorch provides a set of abstractions and features that can help build more complex models, with support for tensors and neural networks.

Masked autoencoder (MAE) is a recently widely used self-supervised learning method that has achieved great success in NLP and computer vision. However, the potential advantages of masked pre-training ...

PyTorch has introduced torchcodec, a machine learning library designed specifically to decode videos into PyTorch tensors. This new tool bridges the gap between video processing and deep learning ...

Autoencoders have proven successful across diverse applications such as data reconstruction, anomaly detection, and feature extraction, however, these advancements remain largely dispersed among ...

PyTorch often creates broadcasted tensors during its backward pass to save on memory, even if the user does not use braodcasting in their code. An example: the backward derivative of .sum() is a ...