Category: PyTorch

Two-dimensional tensor is similar to the two-dimensional metrics. A two-dimensional metrics have n number of rows and n number of columns. Similarly, two-dimensional tensor has n rows and n columns also. A two-dimensional tensor has the following representation A gray scalar image is a two-dimensional matrix of pixels. Each pixel’s intensity denoted by a numeric value…

We know Tensor have different types of dimensions such as zero dimension, one dimension, and multi-dimensional. Vectors are a one-dimensional tensor, and to manipulate them several operations available. Vector operations are of different types such as mathematical operation, dot product, and linspace. Vectors play a vital role in deep learning. In deep learning neural network,…

As we know, PyTorch has been embraced by Deep learning world for the ability to conveniently define neural network. Neural network is fundamentally structured to sensors, and PyTorch is also built around sensors. There tends to be a significant boost in performance. Vaguely a tensor is a generalization of matrices. 1D-Tensor is similar to 1D- matrix. In one dimensional…

Tensors are the key components of Pytorch. We can say PyTorch is wholly based on the Tensors. In mathematics, a rectangular array of number is called metrics. In NumPy library, these metrics called ndaaray. In PyTorch, it is known as Tensor. A tensor is an n-dimensional data container. For example, In PyTorch, 1d-tensor is a…

It is required to understand the difference between the PyTorch and TensorFlow for starting a new project. Libraries play a crucial role when developers decide to work in deep learning or machine learning researches. According to a survey, there are 1,616 ML developers and data scientists who are using PyTorch and 3.4 ML developers who are using TensorFlow. We…

It is essential to understand all the basic concepts which are required to work with PyTorch. PyTorch is completely based on Tensors. Tensor has operations to perform. Apart from these, there are lots of other concepts which are required to perform the task. Now, understand all the concepts one by one to gain deep knowledge…

PyTorch provides the torch.nn module to help us in creating and training of the neural network. We will first train the basic neural network on the MNIST dataset without using any features from these models. We will use only the basic PyTorch tensor functionality and then we will incrementally add one feature from torch.nn at…

PyTorch is an optimized tensor library for deep learning using CPUs and GPUs. PyTorch has a rich set of packages which are used to perform deep learning concepts. These packages help us in optimization, conversion, and loss calculation, etc. Let’s get a brief knowledge of these packages. S.No Name Description 1. Torch The torch package…

For installation, first, you have to choose your preference and then run the install command. You can start installation locally or with a cloud partner. In the below diagram, Stable shows the most currently supported and tested version of PyTorch (1.1), which is suitable for many users. If you want the latest 1.1 builds but…

PyTorch is a small part of a computer software which is based on Torch library. It is a Deep Learning framework introduced by Facebook. PyTorch is a Machine Learning Library for Python programming language which is used for applications such as Natural Language Processing. The high-level features which are provided by PyTorch are as follows: PyTorch was developed to provide high flexibility and speed…