PyTorch

Understanding the differences and use Cases of torch.Tensor.max and torch.max in PyTorch

torch.Tensor.max is an instance method that applies directly to a torch.Tensor object and is mainly used to find maximum values within a single tensor. torch.max is a module-level function used to compare two tensors element-wise.

PyTorch

slice_scatter op in PyTorch

`torch.slice_scatter` is a tensor manipulation function in PyTorch that allows us to embed values from one tensor into another at specified locations. This operation generates a new tensor with fresh storage rather than modifying the original tensor.

PyTorch

Designing ResNet50 in PyTorch

The core architectural innovation is the use of residual blocks with shortcut connections (or skip connections).

Deep Learning

Implementing Simple CNN model in PyTorch

In this OpenGenus article, we will learn about implementing a simple CNN model using PyTorch Deep Learning framework.

PyTorch

Internal Implementation of Tensors in PyTorch

In this OpenGenusarticle, we'll delve into the internals of how tensors are implemented in PyTorch, referencing the code from various components of the PyTorch source code repository.

Deep Learning

as_strided op in PyTorch

So you've now finally begun working towards building your first network in PyTorch. You're working with tensors in the dataset, you wish to alter their shape, or do some form of operations. The as_strided() function in PyTorch can be very useful for this.

PyTorch

Build torchvision from source

In this article, we have explained how to build torchvision from source step by step.

Machine Learning (ML)

Differences between Torch and PyTorch deep learning libraries

We have explored some of the differences between two popular frameworks namely Torch and PyTorch from the view of common origin, current development status, source code and implementation, usage, performance and ONNX support. As development of Torch has been paused, you should go with PyTorch

Machine Learning (ML)

PyTorch vs TensorFlow

We have compared PyTorch and TensorFlow on the basis of various metrics to help you determine the framework you should go forward with. In short, TensorFlow gives you more control and high computational efficiency while PyTorch gives you the simplicity to develop applications.