Pytorch Tensors Explained | Tensors in Pytorch | Pytorch Tutorial | Intellipaat
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In this video, on what tensors are in PyTorch, we will discuss tensors and how they differ from the existing data types. In this part, we will learn how various operations can be performed on Tensors.
We discuss various implementations of tensors like creating empty or uninitialized tensors, explicitly defining the data types, converting list or numpy arrays to tensors and so on.
Below are the concepts covered in this 'Tensors in PyTorch Tutorial':
00:00 - Introduction
01:00 - What Are Tensors in Pytorch?
01:35 - Installing and importing PyTorch
02:10 - Creating Tensors
04:08 - Creating random Tensors
08:28 - Numpy array to Tensors
09:25 - In place operators
11:59 - Reshaping the Tensors
13:35 - Tensor Broadcasting
17:35 - Vector multiplication
19:15 - Transpose
21:05 - Indexing and Slicing
24:05 - Reversing
✅ What is a tensor in pytorch?
Tensors are the core data type of the PyTorch framework. Tensors are the ultimate storage solution for numbers. They can hold all these different arrays in multiple dimensions. Tensors represent the physical properties just like scalars and vectors.
✅ Why do we need tensors in pytorch?
Preprocessing, modeling, and model deployment are all possible with Pytorch's Tensors. A basic Pytorch building block, a tensor is essentially an array of Numpy values. Its primary application is the conversion of images and audio into mathematical forms for use in computer processing.
✅ What is an example of Tensor?
A simple example of a Tensor in PyTorch would be a vector. A vector is a one-dimensional tensor whereas a matrix is a two-dimensional tensor, and an image is a three-dimensional tensor. This is a simple example of Tensor in PyTorch.
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