This course teaches you how to create, manipulate, and understand tensors for machine learning using PyTorch.

Manipulate Tensor in PyTorch (Mini Course)_468 x 60.png

pytorch.tar.gz

pytorch

Tensors form the fundamental building block of the PyTorch ecosystem. Various components, such as network layers, loss function, gradient descent, and optimizer rely on underlying tensor operations.

In this course, you will get a comprehensive overview of tensors starting with how to create one. You will learn the many different types of tensors as well as how to concatenate and manipulate them by selecting elements, changing the shape, and more.

By the end of this course, you will have a basic understanding of how to work with PyTorch tensors and what they allow you to do.

Basic Pytorch Tensor Manipulation for Machine Learning

## What is PyTorch

[PyTorch](https://pytorch.org/) is an open-source machine learning library developed by Facebook that supports multiple languages, such as Python and C++. PyTorch is among the most famous and widely used libraries for machine learning in the world. Many software tools and products are built on top of PyTorch, including Tesla Autopilot, Uber’s Pyro, and HuggingFace’s Transformers.

## What is a PyTorch `tensor`

In short, a PyTorch `tensor` is an n-dimensional array that is the same as a NumPy array or TensorFlow tensor. You can consider a rank 0 tensor as a scalar, a rank 1 tensor as a vector, and a rank 2 tensor as a matrix. For higher-dimensional, they are rank `n` tensor.
This difference between the ranks is shown in the figure below.



## Course learning goal
Tensors form the fundamental building block of the PyTorch ecosystem. Various components, such as network layers, loss functions, gradient descent, and optimizers rely on underlying tensor operations.

This course aims to familiarize the learner with tensors, as used in PyTorch, through hands-on exercises.

## Prerequisites required

To get the most out of the course, you'll need a basic knowledge of Python programming. You do not need prior experience with PyTorch or other machine learning libraries.


# What is PyTorch

[PyTorch](https://pytorch.org/) is an open-source machine learning library developed by Facebook that supports multiple languages, such as Python and C++. PyTorch is among the most famous and widely used libraries for machine learning in the world. Many software tools and products are built on top of PyTorch, including Tesla Autopilot, Uber’s Pyro, and HuggingFace’s Transformers.

# What is a PyTorch `tensor`

In short, a PyTorch `tensor` is an n-dimensional array that is the same as a NumPy array or TensorFlow tensor. You can consider a rank 0 tensor as a scalar, a rank 1 tensor as a vector, and a rank 2 tensor as a matrix. For higher-dimensional, they are rank `n` tensor.
This difference between the ranks is shown in the figure below.



# Course learning goal
Tensors form the fundamental building block of the PyTorch ecosystem. Various components, such as network layers, loss functions, gradient descent, and optimizers rely on underlying tensor operations.

This course aims to familiarize the learner with tensors, as used in PyTorch, through hands-on exercises.

# Prerequisites required

To get the most out of the course, you'll need a basic knowledge of Python programming. You do not need prior experience with PyTorch or other machine learning libraries.


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